Basten7 · August 11, 2025 08:24 · Basten7 · Aug 11, 2025 · mchaliadzinau · Oct 12, 2025
diff --git a/ggml-metal-optimized-4.m b/ggml-metal-optimized-4.m
 #import "ggml-metal.h"

 #import "ggml-impl.h"
 #import "ggml-backend-impl.h"
 #import "ggml-metal-impl.h"

 #import <Foundation/Foundation.h>

 #import <Metal/Metal.h>

 #undef MIN
 #undef MAX
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 #define MAX(a, b) ((a) > (b) ? (a) : (b))

 // max memory buffers that can be mapped to the device
 #define GGML_METAL_MAX_BUFFERS 64

 // max number of MTLCommandBuffer used to submit a graph for processing
 #define GGML_METAL_MAX_COMMAND_BUFFERS 8

 #ifndef TARGET_OS_VISION
 #define TARGET_OS_VISION 0
 #endif

 // create residency sets only on macOS >= 15.0
 #if !TARGET_CPU_X86_64 && TARGET_OS_OSX && __MAC_OS_X_VERSION_MAX_ALLOWED >= 150000 || \
    TARGET_OS_IOS && __IPHONE_OS_VERSION_MAX_ALLOWED >= 180000 || \
    TARGET_OS_TV && __TV_OS_VERSION_MAX_ALLOWED >= 180000 || \
    TARGET_OS_VISION && __VISION_OS_VERSION_MAX_ALLOWED >= 200000
 #define GGML_METAL_HAS_RESIDENCY_SETS 1
 #endif

 // globals

 // overload of MTLGPUFamilyMetal3 (not available in some environments)
 static const NSInteger MTLGPUFamilyMetal3_GGML = 5001;

 // initialized in ggml_backend_metal_reg
 static struct ggml_backend_reg    g_ggml_backend_metal_reg;
 static struct ggml_backend_device g_ggml_backend_metal_device;

 // information about a Metal device
 // note: assumes single GPU device - the default one
 // TODO: support multiple GPU devices
 static struct ggml_backend_metal_device_context {
    id<MTLDevice>  mtl_device;
    int            mtl_device_ref_count;
    id<MTLLibrary> mtl_library;

    NSLock * mtl_lock;

    bool has_simdgroup_reduction;
    bool has_simdgroup_mm;
    bool has_residency_sets;
    bool has_bfloat;
    bool use_bfloat;
    bool use_fusion;

    int debug_fusion;

    // how many times a given op was fused
    uint64_t fuse_cnt[GGML_OP_COUNT];

    size_t max_size;

    char name[128];
 } g_ggml_ctx_dev_main = {
    /*.mtl_device              =*/ nil,
    /*.mtl_device_ref_count    =*/ 0,
    /*.mtl_library             =*/ nil,
    /*.mtl_lock                =*/ nil,
    /*.has_simdgroup_reduction =*/ false,
    /*.has_simdgroup_mm        =*/ false,
    /*.has_residency_sets      =*/ false,
    /*.has_bfloat              =*/ false,
    /*.use_bfloat              =*/ false,
    /*.use_fusion              =*/ true,
    /*.debug_fusion            =*/ 0,
    /*.fuse_cnt                =*/ { 0 },
    /*.max_size                =*/ 0,
    /*.name                    =*/ "",
 };

 // acquire
 static id<MTLDevice> ggml_backend_metal_device_acq(struct ggml_backend_metal_device_context * ctx) {
    assert(ctx != NULL);

    if (ctx->mtl_lock == nil) {
        ctx->mtl_lock = [[NSLock alloc] init];
    }

    if (ctx->mtl_device == nil) {
                // Enhanced device selection: allow picking GPU by index and prefer largest working set on multi-GPU systems
        NSArray * devices = MTLCopyAllDevices();
        id<MTLDevice> chosen = nil;

        const char * env_idx_c = getenv("GGML_METAL_DEVICE_INDEX");
        if (devices && [devices count] > 0) {
            if (env_idx_c) {
                int env_idx = atoi(env_idx_c);
                if (env_idx >= 0 && env_idx < (int)[devices count]) {
                    chosen = [devices objectAtIndex:env_idx];
                    [chosen retain];
                }
            }

            if (chosen == nil) {
 #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
                if (@available(macOS 10.12, iOS 16.0, *)) {
                    // pick the device with the largest recommendedMaxWorkingSetSize
                    uint64_t best = 0;
                    for (id<MTLDevice> d in devices) {
                        uint64_t ws = d.recommendedMaxWorkingSetSize;
                        if (ws > best) { best = ws; chosen = d; }
                    }
                    [chosen retain];
                } else
 #endif
                {
                    chosen = [devices objectAtIndex:0];
                    [chosen retain];
                }
            }
        }

        if (devices) { [devices release]; }

        if (chosen == nil) {
            // Fallback: default device
            ctx->mtl_device = MTLCreateSystemDefaultDevice();
        } else {
            ctx->mtl_device = chosen;
        }ctx->has_simdgroup_reduction  = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];
        ctx->has_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];

        ctx->has_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];

 #if defined(GGML_METAL_HAS_RESIDENCY_SETS)
        ctx->has_residency_sets = getenv("GGML_METAL_NO_RESIDENCY") == nil;
 #endif

        ctx->has_bfloat  = [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
        ctx->has_bfloat |= [ctx->mtl_device supportsFamily:MTLGPUFamilyApple6];

 #if defined(GGML_METAL_USE_BF16)
        ctx->use_bfloat = ctx->has_bfloat;
 #else
        ctx->use_bfloat = false;
 #endif
        ctx->use_fusion = getenv("GGML_METAL_FUSION_DISABLE") == nil;

        {
            const char * val = getenv("GGML_METAL_FUSION_DEBUG");
            ctx->debug_fusion = val ? atoi(val) : 0;
        }

        memset(ctx->fuse_cnt, 0, sizeof(ctx->fuse_cnt));

        ctx->max_size = ctx->mtl_device.maxBufferLength;

        strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1);
    }

    ctx->mtl_device_ref_count++;

    return ctx->mtl_device;
 }

 // release
 static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_context * ctx) {
    assert(ctx != NULL);
    assert(ctx->mtl_device_ref_count > 0);

    ctx->mtl_device_ref_count--;

    if (ctx->mtl_device_ref_count == 0) {
        if (ctx->debug_fusion > 0) {
            fprintf(stderr, "%s: fusion stats:\n", __func__);
            for (int i = 0; i < GGML_OP_COUNT; i++) {
                if (ctx->fuse_cnt[i] == 0) {
                    continue;
                }

                // note: cannot use ggml_log here
                fprintf(stderr, "%s: - %s: %" PRIu64 "\n", __func__, ggml_op_name((enum ggml_op) i), ctx->fuse_cnt[i]);
            }
        }

        if (ctx->mtl_lock) {
            [ctx->mtl_lock release];
            ctx->mtl_lock = nil;
        }

        if (ctx->mtl_library) {
            [ctx->mtl_library release];
            ctx->mtl_library = nil;
        }

        if (ctx->mtl_device) {
            [ctx->mtl_device release];
            ctx->mtl_device = nil;
        }
    }
 }

 // kernels

 struct ggml_metal_kernel {
    id<MTLComputePipelineState> pipeline;
 };

 enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_ADD,
    GGML_METAL_KERNEL_TYPE_ADD_FUSE_2,
    GGML_METAL_KERNEL_TYPE_ADD_FUSE_3,
    GGML_METAL_KERNEL_TYPE_ADD_FUSE_4,
    GGML_METAL_KERNEL_TYPE_ADD_FUSE_5,
    GGML_METAL_KERNEL_TYPE_ADD_FUSE_6,
    GGML_METAL_KERNEL_TYPE_ADD_FUSE_7,
    GGML_METAL_KERNEL_TYPE_ADD_FUSE_8,
    GGML_METAL_KERNEL_TYPE_ADD_ROW_C4,
    GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_2,
    GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_3,
    GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_4,
    GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_5,
    GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_6,
    GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_7,
    GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_8,
    GGML_METAL_KERNEL_TYPE_SUB,
    GGML_METAL_KERNEL_TYPE_SUB_ROW_C4,
    GGML_METAL_KERNEL_TYPE_MUL,
    GGML_METAL_KERNEL_TYPE_MUL_ROW_C4,
    GGML_METAL_KERNEL_TYPE_DIV,
    GGML_METAL_KERNEL_TYPE_DIV_ROW_C4,
    GGML_METAL_KERNEL_TYPE_ADD_ID,
    GGML_METAL_KERNEL_TYPE_REPEAT_F32,
    GGML_METAL_KERNEL_TYPE_REPEAT_F16,
    GGML_METAL_KERNEL_TYPE_REPEAT_I32,
    GGML_METAL_KERNEL_TYPE_REPEAT_I16,
    GGML_METAL_KERNEL_TYPE_SCALE,
    GGML_METAL_KERNEL_TYPE_SCALE_4,
    GGML_METAL_KERNEL_TYPE_CLAMP,
    GGML_METAL_KERNEL_TYPE_TANH,
    GGML_METAL_KERNEL_TYPE_RELU,
    GGML_METAL_KERNEL_TYPE_SIGMOID,
    GGML_METAL_KERNEL_TYPE_GELU,
    GGML_METAL_KERNEL_TYPE_GELU_4,
    GGML_METAL_KERNEL_TYPE_GELU_ERF,
    GGML_METAL_KERNEL_TYPE_GELU_ERF_4,
    GGML_METAL_KERNEL_TYPE_GELU_QUICK,
    GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,
    GGML_METAL_KERNEL_TYPE_SILU,
    GGML_METAL_KERNEL_TYPE_SILU_4,
    GGML_METAL_KERNEL_TYPE_ELU,
    GGML_METAL_KERNEL_TYPE_ABS,
    GGML_METAL_KERNEL_TYPE_SGN,
    GGML_METAL_KERNEL_TYPE_STEP,
    GGML_METAL_KERNEL_TYPE_HARDSWISH,
    GGML_METAL_KERNEL_TYPE_HARDSIGMOID,
    GGML_METAL_KERNEL_TYPE_EXP,
    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,
    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,
    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,
    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4,
    GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,
    GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_F16,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_F32,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_F16,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_BF16,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_Q8_0,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_Q4_0,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_Q4_1,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_Q5_0,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_Q5_1,
    GGML_METAL_KERNEL_TYPE_SET_ROWS_IQ4_NL,
    GGML_METAL_KERNEL_TYPE_RMS_NORM,
    GGML_METAL_KERNEL_TYPE_RMS_NORM_MUL,
    GGML_METAL_KERNEL_TYPE_RMS_NORM_MUL_ADD,
    GGML_METAL_KERNEL_TYPE_L2_NORM,
    GGML_METAL_KERNEL_TYPE_GROUP_NORM,
    GGML_METAL_KERNEL_TYPE_NORM,
    GGML_METAL_KERNEL_TYPE_SSM_CONV_F32,
    GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32,
    GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32_GROUP,
    GGML_METAL_KERNEL_TYPE_RWKV_WKV6_F32,
    GGML_METAL_KERNEL_TYPE_RWKV_WKV7_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32_C4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_C4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW,
    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_C4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW,
    GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,
  //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW,
  //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4,
  //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F16,
    GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32,
    GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16,
    GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F32,
    GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F16,
    GGML_METAL_KERNEL_TYPE_ROPE_VISION_F32,
    GGML_METAL_KERNEL_TYPE_ROPE_VISION_F16,
    GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32,
    GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16,
    GGML_METAL_KERNEL_TYPE_IM2COL_F16,
    GGML_METAL_KERNEL_TYPE_IM2COL_F32,
    GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16,
    GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32,
    GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F32_F32,
    GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F16_F32,
    GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
    GGML_METAL_KERNEL_TYPE_PAD_F32,
    GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32,
    GGML_METAL_KERNEL_TYPE_ARANGE_F32,
    GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
    GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
    GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
    GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H64,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H96,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H192,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_HK192_HV128,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_HK576_HV512,
    GGML_METAL_KERNEL_TYPE_SET_I32,
    GGML_METAL_KERNEL_TYPE_SET_F32,
    GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
    GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
    GGML_METAL_KERNEL_TYPE_CPY_F32_BF16,
    GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
    GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
    GGML_METAL_KERNEL_TYPE_CPY_BF16_F32,
    GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16,
    GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
    GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,
    GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,
    GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,
    GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,
    GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,
    GGML_METAL_KERNEL_TYPE_CPY_Q4_0_F32,
    GGML_METAL_KERNEL_TYPE_CPY_Q4_0_F16,
    GGML_METAL_KERNEL_TYPE_CPY_Q4_1_F32,
    GGML_METAL_KERNEL_TYPE_CPY_Q4_1_F16,
    GGML_METAL_KERNEL_TYPE_CPY_Q5_0_F32,
    GGML_METAL_KERNEL_TYPE_CPY_Q5_0_F16,
    GGML_METAL_KERNEL_TYPE_CPY_Q5_1_F32,
    GGML_METAL_KERNEL_TYPE_CPY_Q5_1_F16,
    GGML_METAL_KERNEL_TYPE_CPY_Q8_0_F32,
    GGML_METAL_KERNEL_TYPE_CPY_Q8_0_F16,
    GGML_METAL_KERNEL_TYPE_CONCAT,
    GGML_METAL_KERNEL_TYPE_SQR,
    GGML_METAL_KERNEL_TYPE_SQRT,
    GGML_METAL_KERNEL_TYPE_SIN,
    GGML_METAL_KERNEL_TYPE_COS,
    GGML_METAL_KERNEL_TYPE_NEG,
    GGML_METAL_KERNEL_TYPE_REGLU,
    GGML_METAL_KERNEL_TYPE_GEGLU,
    GGML_METAL_KERNEL_TYPE_SWIGLU,
    GGML_METAL_KERNEL_TYPE_SWIGLU_OAI,
    GGML_METAL_KERNEL_TYPE_GEGLU_ERF,
    GGML_METAL_KERNEL_TYPE_GEGLU_QUICK,
    GGML_METAL_KERNEL_TYPE_SUM_ROWS,
    GGML_METAL_KERNEL_TYPE_MEAN,
    GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,
    GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,
    GGML_METAL_KERNEL_TYPE_ARGMAX,

    GGML_METAL_KERNEL_TYPE_COUNT
 };

 //
 // ggml_metal_heap
 //

 struct ggml_metal_heap {
    // number of times the heap was unused
    int n_unused;

    // total number of buffer allocations in this heap across all computes
    int64_t n_alloc;

    // current offset in the heap - we reset this after each node in order to reuse the memory
    size_t offs;

    // the currently allocated MTLBuffer objects in this heap
    id<MTLHeap> obj;

    NSMutableArray * bufs;
 };

 static struct ggml_metal_heap * ggml_metal_heap_init(id<MTLDevice> device, size_t size) {
    struct ggml_metal_heap * heap = calloc(1, sizeof(struct ggml_metal_heap));

    MTLHeapDescriptor * desc = [[MTLHeapDescriptor alloc] init];
    desc.storageMode  = MTLStorageModePrivate;
    desc.cpuCacheMode = MTLCPUCacheModeDefaultCache;
    desc.type         = MTLHeapTypePlacement;
    desc.size         = size;

    heap->n_unused = 0;
    heap->n_alloc = 0;

    heap->obj = [device newHeapWithDescriptor:desc];
    if (!heap->obj) {
        GGML_LOG_ERROR("%s: error: failed to create MTLHeap with size %zu\n", __func__, size);

        free(heap);

        return false;
    }

    [desc release];

    heap->bufs = [[NSMutableArray alloc] init];

    return heap;
 }

 static void ggml_metal_heap_reset(struct ggml_metal_heap * heap) {
    heap->offs = 0;

    // count how many graph computes the heap ended up being unused
    if ([heap->bufs count] > 0) {
        heap->n_unused = 0;
    } else {
        heap->n_unused++;
    }

    for (id<MTLBuffer> buf in heap->bufs) {
        [buf release];
    }
    [heap->bufs removeAllObjects];

    // tell the OS that it can reuse this memory if needed
    // ref: https://developer.apple.com/documentation/metal/mtlpurgeablestate?language=objc
    [heap->obj setPurgeableState:MTLPurgeableStateVolatile];
 }

 static void ggml_metal_heap_free(struct ggml_metal_heap * heap) {
    if (heap == nil) {
        return;
    }

    ggml_metal_heap_reset(heap);

    [heap->obj  release];
    [heap->bufs release];

    free(heap);
 }

 @interface ggml_metal_heap_ptr : NSObject

 @property (nonatomic, assign) struct ggml_metal_heap * data;

 @end

 @implementation ggml_metal_heap_ptr
 @end

 //
 // ggml_metal_mem_pool
 //

 struct ggml_metal_mem_pool {
    id<MTLDevice> device;

    int n_heaps; // total number of heaps ever created (including those that were removed)

    NSMutableArray * heaps;
    NSMutableArray * heaps_to_remove;
 };

 static struct ggml_metal_mem_pool * ggml_metal_mem_pool_init(void) {
    struct ggml_metal_mem_pool * mem_pool = calloc(1, sizeof(struct ggml_metal_mem_pool));

    mem_pool->n_heaps = 0;

    mem_pool->heaps           = [[NSMutableArray alloc] init];
    mem_pool->heaps_to_remove = [[NSMutableArray alloc] init];

    return mem_pool;
 }

 static void ggml_metal_mem_pool_free(struct ggml_metal_mem_pool * mem_pool) {
    GGML_LOG_DEBUG("%s: freeing memory pool, num heaps = %zu (total = %d)\n", __func__, [mem_pool->heaps count], mem_pool->n_heaps);

    size_t size_all = 0;
    size_t size_cur = 0;

    for (ggml_metal_heap_ptr * ptr in mem_pool->heaps) {
        GGML_LOG_DEBUG("%s:   heap: %p\n",                __func__, (void *) ptr.data);
        GGML_LOG_DEBUG("%s:     n_alloc:  %" PRId64 "\n", __func__, ptr.data->n_alloc);
        GGML_LOG_DEBUG("%s:     n_unused: %d\n",          __func__, ptr.data->n_unused);
        GGML_LOG_DEBUG("%s:     size:     %.2f MiB\n",    __func__, [ptr.data->obj size] / 1024.0 / 1024.0);
        GGML_LOG_DEBUG("%s:     bufs:     %zu\n",         __func__, [ptr.data->bufs count]);

        if ([ptr.data->bufs count] > 0) {
            size_cur += [ptr.data->obj size];
        }
        size_all += [ptr.data->obj size];

        ggml_metal_heap_free(ptr.data);
        [ptr release];
    }
    [mem_pool->heaps           release];
    [mem_pool->heaps_to_remove release];

    if (size_all > 0) {
        GGML_LOG_DEBUG("%s:   size_all: %.2f MiB\n", __func__, size_all / 1024.0 / 1024.0);
        GGML_LOG_DEBUG("%s:   size_cur: %.2f MiB\n", __func__, size_cur / 1024.0 / 1024.0);
    }

    free(mem_pool);
 }

 static void ggml_metal_mem_pool_reset(struct ggml_metal_mem_pool * mem_pool) {
    for (NSUInteger i = 0; i < [mem_pool->heaps count]; i++) {
        ggml_metal_heap_ptr * ptr = [mem_pool->heaps objectAtIndex:i];

        struct ggml_metal_heap * heap = ptr.data;
        ggml_metal_heap_reset(heap);

        // if the heap hasn't been used for a while, remove it
        if (heap->n_unused >= 128) {
            [mem_pool->heaps_to_remove addObject:@(i)];
        }
    }

    if (mem_pool->heaps_to_remove.count > 0) {
        // remove in reverse order
        for (NSUInteger i = [mem_pool->heaps_to_remove count] - 1; ; --i) {
            NSUInteger index = [[mem_pool->heaps_to_remove objectAtIndex:i] intValue];
            ggml_metal_heap_ptr * ptr = [mem_pool->heaps objectAtIndex:index];

            struct ggml_metal_heap * heap = ptr.data;
            ggml_metal_heap_free(heap);

            [mem_pool->heaps removeObjectAtIndex:index];
            [ptr release];

            if (i == 0) {
                break;
            }
        }

        [mem_pool->heaps_to_remove removeAllObjects];
    }
 }

 static void ggml_metal_mem_pool_clear(struct ggml_metal_mem_pool * mem_pool) {
    for (ggml_metal_heap_ptr * ptr in mem_pool->heaps) {
        ptr.data->offs = 0;
    }
 }

 static id<MTLBuffer> ggml_metal_mem_pool_alloc(struct ggml_metal_mem_pool * mem_pool, size_t size) {
    const size_t alignment = 256;

    const size_t size_aligned = GGML_PAD(size, alignment);

    // try one of the existing heaps
    for (ggml_metal_heap_ptr * ptr in mem_pool->heaps) {
        struct ggml_metal_heap * heap = ptr.data;
        if (heap->offs + size_aligned <= [heap->obj size]) {
            // if this is the first buffer in the heap for the current command buffer, tell the OS that
            //   it cannot free the memory used by the heap
            // ref: https://developer.apple.com/documentation/metal/mtlpurgeablestate?language=objc
            if ([heap->bufs count] == 0) {
                [heap->obj setPurgeableState:MTLPurgeableStateNonVolatile];
            }

            id<MTLBuffer> buf = [heap->obj newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate offset:heap->offs];
            if (buf == nil) {
                GGML_LOG_ERROR("%s: error: failed to create MTLBuffer with size %zu\n", __func__, size_aligned);
                return nil;
            }

            heap->n_alloc++;
            heap->offs += size_aligned;

            [heap->bufs addObject:buf];

            return buf;
        }
    }

    // create a new heap that can fit this buffer
    ggml_metal_heap_ptr * heap_ptr = [ggml_metal_heap_ptr new];

    struct ggml_metal_heap * heap = ggml_metal_heap_init(mem_pool->device, size_aligned);
    if (heap == NULL) {
        GGML_LOG_ERROR("%s: error: failed to create heap of size %zu\n", __func__, size_aligned);
        return NULL;
    }

    //GGML_LOG_DEBUG("%s: creating new heap of size %zu, got %zu\n", __func__, size_aligned, [heap->obj size]);

    heap_ptr.data = heap;
    ggml_metal_heap_reset(heap);

    [heap->obj setPurgeableState:MTLPurgeableStateNonVolatile];
    id<MTLBuffer> buf = [heap->obj newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate offset:heap->offs];
    if (buf == nil) {
        GGML_LOG_ERROR("%s: error: failed to create MTLBuffer with size %zu\n", __func__, size_aligned);
        return NULL;
    }

    heap->n_alloc++;
    heap->offs += size_aligned;

    [heap->bufs addObject:buf];

    [mem_pool->heaps addObject:heap_ptr];
    mem_pool->n_heaps++;

    return buf;
 }

 struct ggml_metal_command_buffer {
    id<MTLCommandBuffer> obj;

    // each command buffer has a memory pool from which it can allocate temporary buffers during the compute
    struct ggml_metal_mem_pool * mem_pool;
 };

 struct ggml_backend_metal_context {
    id<MTLDevice>       device;
    id<MTLCommandQueue> queue;

    dispatch_queue_t d_queue;

    struct ggml_metal_kernel kernels[GGML_METAL_KERNEL_TYPE_COUNT];

    // capture state
    bool capture_next_compute;
    bool capture_started;

    id<MTLCaptureScope> capture_scope;

    // command buffer state
    int n_cb;           // number of extra threads used to submit the command buffers
    int n_nodes_0;      // number of nodes submitted by the main thread
    int n_nodes_1;      // remaining number of nodes submitted by the n_cb threads
    int n_nodes_per_cb;

    struct ggml_cgraph * gf;

    // the callback given to the thread pool
    void (^encode_async)(size_t ith);

    // n_cb command buffers + 1 used by the main thread
    struct ggml_metal_command_buffer cmd_bufs[GGML_METAL_MAX_COMMAND_BUFFERS + 1];

    // abort ggml_metal_graph_compute if callback returns true
    ggml_abort_callback abort_callback;
    void *              abort_callback_data;
 };

 // MSL code
 // TODO: move the contents here when ready
 //       for now it is easier to work in a separate file
 // static NSString * const msl_library_source = @"see metal.metal";

 #if !GGML_METAL_EMBED_LIBRARY
 // Here to assist with NSBundle Path Hack
 @interface GGMLMetalClass : NSObject
 @end
 @implementation GGMLMetalClass
 @end
 #endif

 static void * ggml_metal_host_malloc(size_t n) {
    void * data = NULL;

 #if TARGET_OS_OSX
    kern_return_t err = vm_allocate((vm_map_t) mach_task_self(), (void *) &data, n, VM_FLAGS_ANYWHERE);
    if (err != KERN_SUCCESS) {
        GGML_LOG_ERROR("%s: error: vm_allocate failed\n", __func__);
        return NULL;
    }
 #else
    const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
    if (result != 0) {
        GGML_LOG_ERROR("%s: error: posix_memalign failed\n", __func__);
        return NULL;
    }
 #endif

    return data;
 }

 // load library
 //
 // - first check if the library is embedded
 // - then check if the library is in the bundle
 // - if not found, load the source and compile it
 // - if that fails, return NULL
 static id<MTLLibrary> ggml_metal_load_library(id<MTLDevice> device, bool use_bfloat) {
    id<MTLLibrary> metal_library = nil;
    NSError * error = nil;
    NSString * src = nil;

 #if GGML_METAL_EMBED_LIBRARY
    GGML_LOG_INFO("%s: using embedded metal library\n", __func__);

    extern const char ggml_metallib_start[];
    extern const char ggml_metallib_end[];

    src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];

 #else

 #ifdef SWIFT_PACKAGE
    NSBundle * bundle = SWIFTPM_MODULE_BUNDLE;
 #else
    NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
 #endif

    NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
    if (path_lib == nil) {
        // Try to find the resource in the directory where the current binary located.
        NSString * current_binary = [[NSProcessInfo processInfo] arguments][0];
        NSString * bin_dir = [current_binary stringByDeletingLastPathComponent];
        NSString * default_metallib_path = [NSString pathWithComponents:@[bin_dir, @"default.metallib"]];
        if ([[NSFileManager defaultManager] isReadableFileAtPath:default_metallib_path]) {
            GGML_LOG_INFO("%s: found '%s'\n", __func__, [default_metallib_path UTF8String]);
            NSDictionary * atts = [[NSFileManager defaultManager] attributesOfItemAtPath:default_metallib_path error:&error];
            if (atts && atts[NSFileType] == NSFileTypeSymbolicLink) {
                // Optionally, if this is a symlink, try to resolve it.
                default_metallib_path = [[NSFileManager defaultManager] destinationOfSymbolicLinkAtPath:default_metallib_path error:&error];
                if (default_metallib_path && [default_metallib_path length] > 0 && ![[default_metallib_path substringToIndex:1] isEqualToString:@"/"]) {
                    // It is a relative path, adding the binary directory as directory prefix.
                    default_metallib_path = [NSString pathWithComponents:@[bin_dir, default_metallib_path]];
                }
                if (!default_metallib_path || ![[NSFileManager defaultManager] isReadableFileAtPath:default_metallib_path]) {
                    // Link to the resource could not be resolved.
                    default_metallib_path = nil;
                } else {
                    GGML_LOG_INFO("%s: symlink resolved '%s'\n", __func__, [default_metallib_path UTF8String]);
                }
            }
        } else {
            // The resource couldn't be found in the binary's directory.
            default_metallib_path = nil;
        }
        path_lib = default_metallib_path;
    }

    if (path_lib != nil) {
        // pre-compiled library found
        NSURL * libURL = [NSURL fileURLWithPath:path_lib];
        GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);

        metal_library = [device newLibraryWithURL:libURL error:&error];
        if (error) {
            GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
            return NULL;
        }
    } else {
        GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);

        NSString * path_source;
        NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];

        GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");

        if (path_resource) {
            path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
        } else {
            path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
        }

        if (path_source == nil) {
            GGML_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
            path_source = @"ggml-metal.metal";
        }

        GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);

        src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
        if (error) {
            GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
            return NULL;
        }
    }
 #endif

    if (!metal_library) {
        @autoreleasepool {
            // dictionary of preprocessor macros
            NSMutableDictionary * prep = [NSMutableDictionary dictionary];

            if (use_bfloat) {
                [prep setObject:@"1" forKey:@"GGML_METAL_USE_BF16"];
            }

 #if GGML_METAL_EMBED_LIBRARY
            [prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"];
 #endif

            MTLCompileOptions * options = [MTLCompileOptions new];
            options.preprocessorMacros = prep;

            //[options setFastMathEnabled:false];

            metal_library = [device newLibraryWithSource:src options:options error:&error];
            if (error) {
                GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
                return NULL;
            }

 #if !__has_feature(objc_arc)
            [options release];
 #endif
        }
    }

 #if GGML_METAL_EMBED_LIBRARY
    [src release];
 #endif // GGML_METAL_EMBED_LIBRARY

    return metal_library;
 }

 static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t dev) {
    GGML_LOG_INFO("%s: allocating\n", __func__);

 #if TARGET_OS_OSX && !GGML_METAL_NDEBUG
    // Show all the Metal device instances in the system
    NSArray * devices = MTLCopyAllDevices();
    for (id<MTLDevice> device in devices) {
        GGML_LOG_INFO("%s: found device: %s\n", __func__, [[device name] UTF8String]);
    }
    [devices release]; // since it was created by a *Copy* C method
 #endif

    // init context
    struct ggml_backend_metal_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_context));
    struct ggml_backend_metal_device_context * ctx_dev = dev->context;

    id<MTLDevice> device = ctx_dev->mtl_device;

    GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]);

    ctx->device = device;
    ctx->queue = [device newCommandQueue];
    if (ctx->queue == nil) {
        GGML_LOG_ERROR("%s: error: failed to create command queue\n", __func__);
        return NULL;
    }

    ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);

    // load library
    {
        [ctx_dev->mtl_lock lock];

        if (ctx_dev->mtl_library == nil) {
            ctx_dev->mtl_library = ggml_metal_load_library(device, ctx_dev->use_bfloat);
        }

        [ctx_dev->mtl_lock unlock];
    }

    id<MTLLibrary> metal_library = ctx_dev->mtl_library;
    if (metal_library == nil) {
        GGML_LOG_ERROR("%s: error: metal library is nil\n", __func__);
        return NULL;
    }

    // print MTL GPU family:
    GGML_LOG_INFO("%s: GPU name:   %s\n", __func__, [[device name] UTF8String]);

    // determine max supported GPU family
    // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
    // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
    {
        for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) {
            if ([device supportsFamily:i]) {
                GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d  (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i);
                break;
            }
        }

        for (int i = MTLGPUFamilyCommon1 + 5; i >= MTLGPUFamilyCommon1; --i) {
            if ([device supportsFamily:i]) {
                GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i);
                break;
            }
        }

        for (int i = MTLGPUFamilyMetal3_GGML + 5; i >= MTLGPUFamilyMetal3_GGML; --i) {
            if ([device supportsFamily:i]) {
                GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d  (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3_GGML + 3, i);
                break;
            }
        }
    }

    GGML_LOG_INFO("%s: simdgroup reduction   = %s\n", __func__, ctx_dev->has_simdgroup_reduction     ? "true" : "false");
    GGML_LOG_INFO("%s: simdgroup matrix mul. = %s\n", __func__, ctx_dev->has_simdgroup_mm            ? "true" : "false");
    GGML_LOG_INFO("%s: has residency sets    = %s\n", __func__, ctx_dev->has_residency_sets          ? "true" : "false");
    GGML_LOG_INFO("%s: has bfloat            = %s\n", __func__, ctx_dev->has_bfloat                  ? "true" : "false");
    GGML_LOG_INFO("%s: use bfloat            = %s\n", __func__, ctx_dev->use_bfloat                  ? "true" : "false");
    GGML_LOG_INFO("%s: hasUnifiedMemory      = %s\n", __func__, ctx_dev->mtl_device.hasUnifiedMemory ? "true" : "false");

    ctx->capture_next_compute = false;
    ctx->capture_started = false;
    ctx->capture_scope = nil;

    ctx->gf = nil;
    ctx->encode_async = nil;
    for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) {
        ctx->cmd_bufs[i].obj = nil;

        ctx->cmd_bufs[i].mem_pool = ggml_metal_mem_pool_init();
        ctx->cmd_bufs[i].mem_pool->device = device;
    }

 #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
    if (@available(macOS 10.12, iOS 16.0, *)) {
        GGML_LOG_INFO("%s: recommendedMaxWorkingSetSize  = %8.2f MB\n", __func__, device.recommendedMaxWorkingSetSize / 1e6);
    }
 #endif

    // load kernels
    {
        NSError * error = nil;

        for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) {
            ctx->kernels[i].pipeline = nil;
        }

 #define GGML_METAL_ADD_KERNEL(e, name, supported) \
        if (supported) { \
            struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \
            id<MTLFunction> metal_function = [metal_library newFunctionWithName:@"kernel_"#name]; \
            kernel->pipeline = [device newComputePipelineStateWithFunction:metal_function error:&error]; \
            GGML_LOG_DEBUG("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
                    (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \
                    (int) kernel->pipeline.threadExecutionWidth); \
            [metal_function release]; \
            if (error) { \
                GGML_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
                return NULL; \
            } \
        } else { \
            GGML_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \
        }

        const bool has_simdgroup_mm        = ctx_dev->has_simdgroup_mm;
        const bool has_simdgroup_reduction = ctx_dev->has_simdgroup_reduction;
        const bool use_bfloat              = ctx_dev->use_bfloat;

        // simd_sum and simd_max requires MTLGPUFamilyApple7

        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD,                             add,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_FUSE_2,                      add_fuse_2,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_FUSE_3,                      add_fuse_3,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_FUSE_4,                      add_fuse_4,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_FUSE_5,                      add_fuse_5,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_FUSE_6,                      add_fuse_6,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_FUSE_7,                      add_fuse_7,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_FUSE_8,                      add_fuse_8,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW_C4,                      add_row_c4,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_2,               add_row_c4_fuse_2,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_3,               add_row_c4_fuse_3,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_4,               add_row_c4_fuse_4,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_5,               add_row_c4_fuse_5,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_6,               add_row_c4_fuse_6,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_7,               add_row_c4_fuse_7,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_8,               add_row_c4_fuse_8,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUB,                             sub,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUB_ROW_C4,                      sub_row_c4,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL,                             mul,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW_C4,                      mul_row_c4,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                             div,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW_C4,                      div_row_c4,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ID,                          add_id,                          true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32,                      repeat_f32,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16,                      repeat_f16,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32,                      repeat_i32,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I16,                      repeat_i16,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE,                           scale,                           true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4,                         scale_4,                         true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP,                           clamp,                           true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH,                            tanh,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU,                            relu,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID,                         sigmoid,                         true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU,                            gelu,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4,                          gelu_4,                          true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_ERF,                        gelu_erf,                        true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_ERF_4,                      gelu_erf_4,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK,                      gelu_quick,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,                    gelu_quick_4,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU,                            silu,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4,                          silu_4,                          true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ELU,                             elu,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ABS,                             abs,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SGN,                             sgn,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_STEP,                            step,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_HARDSWISH,                       hardswish,                       true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_HARDSIGMOID,                     hardsigmoid,                     true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_EXP,                             exp,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,                    soft_max_f16,                    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,                  soft_max_f16_4,                  has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,                    soft_max_f32,                    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4,                  soft_max_f32_4,                  has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,                   diag_mask_inf,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,                 diag_mask_inf_8,                 true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,                    get_rows_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16,                    get_rows_f16,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16,                   get_rows_bf16,                   use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0,                   get_rows_q4_0,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1,                   get_rows_q4_1,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,                   get_rows_q5_0,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,                   get_rows_q5_1,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,                   get_rows_q8_0,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4,                  get_rows_mxfp4,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,                   get_rows_q2_K,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,                   get_rows_q3_K,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,                   get_rows_q4_K,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K,                   get_rows_q5_K,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,                   get_rows_q6_K,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,                get_rows_iq2_xxs,                true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,                 get_rows_iq2_xs,                 true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,                get_rows_iq3_xxs,                true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,                  get_rows_iq3_s,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,                  get_rows_iq2_s,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,                  get_rows_iq1_s,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,                  get_rows_iq1_m,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,                 get_rows_iq4_nl,                 true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,                 get_rows_iq4_xs,                 true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,                    get_rows_i32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_F32,                    set_rows_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_F16,                    set_rows_f16,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_BF16,                   set_rows_bf16,                   use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_Q8_0,                   set_rows_q8_0,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_Q4_0,                   set_rows_q4_0,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_Q4_1,                   set_rows_q4_1,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_Q5_0,                   set_rows_q5_0,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_Q5_1,                   set_rows_q5_1,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_ROWS_IQ4_NL,                 set_rows_iq4_nl,                 true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM,                        rms_norm,                        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM_MUL,                    rms_norm_mul,                    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM_MUL_ADD,                rms_norm_mul_add,                has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_L2_NORM,                         l2_norm,                         has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM,                      group_norm,                      has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM,                            norm,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_CONV_F32,                    ssm_conv_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32,                    ssm_scan_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32_GROUP,              ssm_scan_f32_group,              true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RWKV_WKV6_F32,                   rwkv_wkv6_f32,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RWKV_WKV7_F32,                   rwkv_wkv7_f32,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,                  mul_mv_f32_f32,                  has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32_C4,               mul_mv_f32_f32_c4,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32,                 mul_mv_bf16_f32,                 has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_C4,              mul_mv_bf16_f32_c4,              use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW,            mul_mv_bf16_f32_1row,            has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4,              mul_mv_bf16_f32_l4,              has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16,                mul_mv_bf16_bf16,                has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,                  mul_mv_f16_f32,                  has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_C4,               mul_mv_f16_f32_c4,               true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW,             mul_mv_f16_f32_1row,             has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4,               mul_mv_f16_f32_l4,               has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,                  mul_mv_f16_f16,                  has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32,                 mul_mv_q4_0_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32,                 mul_mv_q4_1_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,                 mul_mv_q5_0_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,                 mul_mv_q5_1_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,                 mul_mv_q8_0_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32,                mul_mv_mxfp4_f32,                has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2,         mul_mv_ext_f16_f32_r1_2,         has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3,         mul_mv_ext_f16_f32_r1_3,         has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4,         mul_mv_ext_f16_f32_r1_4,         has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_5,         mul_mv_ext_f16_f32_r1_5,         has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_2,        mul_mv_ext_q4_0_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_3,        mul_mv_ext_q4_0_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_4,        mul_mv_ext_q4_0_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_5,        mul_mv_ext_q4_0_f32_r1_5,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_2,        mul_mv_ext_q4_1_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_3,        mul_mv_ext_q4_1_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_4,        mul_mv_ext_q4_1_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_5,        mul_mv_ext_q4_1_f32_r1_5,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_2,        mul_mv_ext_q5_0_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_3,        mul_mv_ext_q5_0_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_4,        mul_mv_ext_q5_0_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_5,        mul_mv_ext_q5_0_f32_r1_5,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_2,        mul_mv_ext_q5_1_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_3,        mul_mv_ext_q5_1_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_4,        mul_mv_ext_q5_1_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_5,        mul_mv_ext_q5_1_f32_r1_5,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_2,        mul_mv_ext_q8_0_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3,        mul_mv_ext_q8_0_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4,        mul_mv_ext_q8_0_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5,        mul_mv_ext_q8_0_f32_r1_5,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2,       mul_mv_ext_mxfp4_f32_r1_2,       has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3,       mul_mv_ext_mxfp4_f32_r1_3,       has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4,       mul_mv_ext_mxfp4_f32_r1_4,       has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5,       mul_mv_ext_mxfp4_f32_r1_5,       has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2,        mul_mv_ext_q4_K_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3,        mul_mv_ext_q4_K_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4,        mul_mv_ext_q4_K_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_5,        mul_mv_ext_q4_K_f32_r1_5,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_2,        mul_mv_ext_q5_K_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_3,        mul_mv_ext_q5_K_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_4,        mul_mv_ext_q5_K_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_5,        mul_mv_ext_q5_K_f32_r1_5,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_2,        mul_mv_ext_q6_K_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_3,        mul_mv_ext_q6_K_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_4,        mul_mv_ext_q6_K_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_5,        mul_mv_ext_q6_K_f32_r1_5,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_2,      mul_mv_ext_iq4_nl_f32_r1_2,      has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_3,      mul_mv_ext_iq4_nl_f32_r1_3,      has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_4,      mul_mv_ext_iq4_nl_f32_r1_4,      has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_5,      mul_mv_ext_iq4_nl_f32_r1_5,      has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32,                 mul_mv_q2_K_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32,                 mul_mv_q3_K_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32,                 mul_mv_q4_K_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32,                 mul_mv_q5_K_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,                 mul_mv_q6_K_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,              mul_mv_iq2_xxs_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,               mul_mv_iq2_xs_f32,               has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,              mul_mv_iq3_xxs_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,                mul_mv_iq3_s_f32,                has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,                mul_mv_iq2_s_f32,                has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,                mul_mv_iq1_s_f32,                has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,                mul_mv_iq1_m_f32,                has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,               mul_mv_iq4_nl_f32,               has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,               mul_mv_iq4_xs_f32,               has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,               mul_mv_id_f32_f32,               has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,               mul_mv_id_f16_f32,               has_simdgroup_reduction);
      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW,          mul_mv_id_f16_f32_1row,          has_simdgroup_reduction);
      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4,            mul_mv_id_f16_f32_l4,            has_simdgroup_reduction);
      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,               mul_mv_id_f16_f16,               has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32,              mul_mv_id_bf16_f32,              has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32,              mul_mv_id_q4_0_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32,              mul_mv_id_q4_1_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,              mul_mv_id_q5_0_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,              mul_mv_id_q5_1_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,              mul_mv_id_q8_0_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32,             mul_mv_id_mxfp4_f32,             has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,              mul_mv_id_q2_K_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,              mul_mv_id_q3_K_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,              mul_mv_id_q4_K_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32,              mul_mv_id_q5_K_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,              mul_mv_id_q6_K_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,           mul_mv_id_iq2_xxs_f32,           has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,            mul_mv_id_iq2_xs_f32,            has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,           mul_mv_id_iq3_xxs_f32,           has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,             mul_mv_id_iq3_s_f32,             has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,             mul_mv_id_iq2_s_f32,             has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,             mul_mv_id_iq1_s_f32,             has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,             mul_mv_id_iq1_m_f32,             has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,            mul_mv_id_iq4_nl_f32,            has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,            mul_mv_id_iq4_xs_f32,            has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,                  mul_mm_f32_f32,                  has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,                  mul_mm_f16_f32,                  has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32,                 mul_mm_bf16_f32,                 has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,                 mul_mm_q4_0_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32,                 mul_mm_q4_1_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,                 mul_mm_q5_0_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,                 mul_mm_q5_1_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,                 mul_mm_q8_0_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,                mul_mm_mxfp4_f32,                has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,                mul_mm_mxfp4_f32,                has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,                 mul_mm_q2_K_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,                 mul_mm_q3_K_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,                 mul_mm_q4_K_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32,                 mul_mm_q5_K_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,                 mul_mm_q6_K_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,              mul_mm_iq2_xxs_f32,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,               mul_mm_iq2_xs_f32,               has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,              mul_mm_iq3_xxs_f32,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,                mul_mm_iq3_s_f32,                has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,                mul_mm_iq2_s_f32,                has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,                mul_mm_iq1_s_f32,                has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,                mul_mm_iq1_m_f32,                has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,               mul_mm_iq4_nl_f32,               has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,               mul_mm_iq4_xs_f32,               has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16,              mul_mm_id_map0_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP1_F32,              mul_mm_id_map1_f32,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F16,               mul_mm_id_f32_f16,               has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F16,               mul_mm_id_f16_f16,               has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F16,              mul_mm_id_bf16_f16,              has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F16,              mul_mm_id_q4_0_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F16,              mul_mm_id_q4_1_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16,              mul_mm_id_q5_0_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16,              mul_mm_id_q5_1_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16,              mul_mm_id_q8_0_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16,             mul_mm_id_mxfp4_f16,             has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16,              mul_mm_id_q2_K_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16,              mul_mm_id_q3_K_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16,              mul_mm_id_q4_K_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F16,              mul_mm_id_q5_K_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F16,              mul_mm_id_q6_K_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F16,           mul_mm_id_iq2_xxs_f16,           has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F16,            mul_mm_id_iq2_xs_f16,            has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F16,           mul_mm_id_iq3_xxs_f16,           has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F16,             mul_mm_id_iq3_s_f16,             has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F16,             mul_mm_id_iq2_s_f16,             has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F16,             mul_mm_id_iq1_s_f16,             has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F16,             mul_mm_id_iq1_m_f16,             has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F16,            mul_mm_id_iq4_nl_f16,            has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F16,            mul_mm_id_iq4_xs_f16,            has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32,                   rope_norm_f32,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16,                   rope_norm_f16,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F32,                  rope_multi_f32,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F16,                  rope_multi_f16,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_VISION_F32,                 rope_vision_f32,                 true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_VISION_F16,                 rope_vision_f16,                 true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32,                   rope_neox_f32,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16,                   rope_neox_f16,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16,                      im2col_f16,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32,                      im2col_f32,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16,                  im2col_ext_f16,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32,                  im2col_ext_f32,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F32_F32,       conv_transpose_1d_f32_f32,       true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F16_F32,       conv_transpose_1d_f16_f32,       true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32,                     upscale_f32,                     true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32,                         pad_f32,                         true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32,              pad_reflect_1d_f32,              true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,          timestep_embedding_f32,          true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32,                      arange_f32,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,             argsort_f32_i32_asc,             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,            argsort_f32_i32_desc,            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,                  leaky_relu_f32,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,          flash_attn_ext_f16_h64,          has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,          flash_attn_ext_f16_h80,          has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,          flash_attn_ext_f16_h96,          has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,         flash_attn_ext_f16_h112,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,         flash_attn_ext_f16_h128,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H192,         flash_attn_ext_f16_h192,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK192_HV128,  flash_attn_ext_f16_hk192_hv128,  has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,         flash_attn_ext_f16_h256,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK576_HV512,  flash_attn_ext_f16_hk576_hv512,  has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64,         flash_attn_ext_bf16_h64,         has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80,         flash_attn_ext_bf16_h80,         has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96,         flash_attn_ext_bf16_h96,         has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112,        flash_attn_ext_bf16_h112,        has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128,        flash_attn_ext_bf16_h128,        has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H192,        flash_attn_ext_bf16_h192,        has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK192_HV128, flash_attn_ext_bf16_hk192_hv128, has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256,        flash_attn_ext_bf16_h256,        has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK576_HV512, flash_attn_ext_bf16_hk576_hv512, has_simdgroup_mm && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64,         flash_attn_ext_q4_0_h64,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80,         flash_attn_ext_q4_0_h80,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96,         flash_attn_ext_q4_0_h96,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112,        flash_attn_ext_q4_0_h112,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128,        flash_attn_ext_q4_0_h128,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H192,        flash_attn_ext_q4_0_h192,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK192_HV128, flash_attn_ext_q4_0_hk192_hv128, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256,        flash_attn_ext_q4_0_h256,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK576_HV512, flash_attn_ext_q4_0_hk576_hv512, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64,         flash_attn_ext_q4_1_h64,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80,         flash_attn_ext_q4_1_h80,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96,         flash_attn_ext_q4_1_h96,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112,        flash_attn_ext_q4_1_h112,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128,        flash_attn_ext_q4_1_h128,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H192,        flash_attn_ext_q4_1_h192,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK192_HV128, flash_attn_ext_q4_1_hk192_hv128, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256,        flash_attn_ext_q4_1_h256,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK576_HV512, flash_attn_ext_q4_1_hk576_hv512, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64,         flash_attn_ext_q5_0_h64,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80,         flash_attn_ext_q5_0_h80,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96,         flash_attn_ext_q5_0_h96,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112,        flash_attn_ext_q5_0_h112,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128,        flash_attn_ext_q5_0_h128,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H192,        flash_attn_ext_q5_0_h192,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK192_HV128, flash_attn_ext_q5_0_hk192_hv128, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256,        flash_attn_ext_q5_0_h256,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK576_HV512, flash_attn_ext_q5_0_hk576_hv512, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64,         flash_attn_ext_q5_1_h64,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80,         flash_attn_ext_q5_1_h80,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96,         flash_attn_ext_q5_1_h96,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112,        flash_attn_ext_q5_1_h112,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128,        flash_attn_ext_q5_1_h128,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H192,        flash_attn_ext_q5_1_h192,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK192_HV128, flash_attn_ext_q5_1_hk192_hv128, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256,        flash_attn_ext_q5_1_h256,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK576_HV512, flash_attn_ext_q5_1_hk576_hv512, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64,         flash_attn_ext_q8_0_h64,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80,         flash_attn_ext_q8_0_h80,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96,         flash_attn_ext_q8_0_h96,         has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112,        flash_attn_ext_q8_0_h112,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128,        flash_attn_ext_q8_0_h128,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H192,        flash_attn_ext_q8_0_h192,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128, flash_attn_ext_q8_0_hk192_hv128, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,        flash_attn_ext_q8_0_h256,        has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK576_HV512, flash_attn_ext_q8_0_hk576_hv512, has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H64,      flash_attn_ext_vec_f16_h64,      has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H64,     flash_attn_ext_vec_bf16_h64,     has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H64,     flash_attn_ext_vec_q4_0_h64,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H64,     flash_attn_ext_vec_q4_1_h64,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H64,     flash_attn_ext_vec_q5_0_h64,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H64,     flash_attn_ext_vec_q5_1_h64,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H64,     flash_attn_ext_vec_q8_0_h64,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H96,      flash_attn_ext_vec_f16_h96,      has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H96,     flash_attn_ext_vec_bf16_h96,     has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H96,     flash_attn_ext_vec_q4_0_h96,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H96,     flash_attn_ext_vec_q4_1_h96,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H96,     flash_attn_ext_vec_q5_0_h96,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H96,     flash_attn_ext_vec_q5_1_h96,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H96,     flash_attn_ext_vec_q8_0_h96,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,     flash_attn_ext_vec_f16_h128,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128,    flash_attn_ext_vec_bf16_h128,    has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128,    flash_attn_ext_vec_q4_0_h128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128,    flash_attn_ext_vec_q4_1_h128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128,    flash_attn_ext_vec_q5_0_h128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128,    flash_attn_ext_vec_q5_1_h128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128,    flash_attn_ext_vec_q8_0_h128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H192,     flash_attn_ext_vec_f16_h192,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H192,    flash_attn_ext_vec_bf16_h192,    has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H192,    flash_attn_ext_vec_q4_0_h192,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H192,    flash_attn_ext_vec_q4_1_h192,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H192,    flash_attn_ext_vec_q5_0_h192,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H192,    flash_attn_ext_vec_q5_1_h192,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H192,    flash_attn_ext_vec_q8_0_h192,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_HK192_HV128,     flash_attn_ext_vec_f16_hk192_hv128,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_HK192_HV128,    flash_attn_ext_vec_bf16_hk192_hv128,    has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_HK192_HV128,    flash_attn_ext_vec_q4_0_hk192_hv128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_HK192_HV128,    flash_attn_ext_vec_q4_1_hk192_hv128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_HK192_HV128,    flash_attn_ext_vec_q5_0_hk192_hv128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_HK192_HV128,    flash_attn_ext_vec_q5_1_hk192_hv128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_HK192_HV128,    flash_attn_ext_vec_q8_0_hk192_hv128,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,     flash_attn_ext_vec_f16_h256,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256,    flash_attn_ext_vec_bf16_h256,    has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256,    flash_attn_ext_vec_q4_0_h256,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256,    flash_attn_ext_vec_q4_1_h256,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256,    flash_attn_ext_vec_q5_0_h256,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256,    flash_attn_ext_vec_q5_1_h256,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256,    flash_attn_ext_vec_q8_0_h256,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_HK576_HV512,     flash_attn_ext_vec_f16_hk576_hv512,     has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_HK576_HV512,    flash_attn_ext_vec_bf16_hk576_hv512,    has_simdgroup_reduction && use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_HK576_HV512,    flash_attn_ext_vec_q4_0_hk576_hv512,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_HK576_HV512,    flash_attn_ext_vec_q4_1_hk576_hv512,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_HK576_HV512,    flash_attn_ext_vec_q5_0_hk576_hv512,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_HK576_HV512,    flash_attn_ext_vec_q5_1_hk576_hv512,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_HK576_HV512,    flash_attn_ext_vec_q8_0_hk576_hv512,    has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_F32,                         set_f32,                         true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_I32,                         set_i32,                         true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,                     cpy_f32_f32,                     true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16,                     cpy_f32_f16,                     true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_BF16,                    cpy_f32_bf16,                    use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,                     cpy_f16_f32,                     true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,                     cpy_f16_f16,                     true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_F32,                    cpy_bf16_f32,                    use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16,                   cpy_bf16_bf16,                   use_bfloat);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,                    cpy_f32_q8_0,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,                    cpy_f32_q4_0,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,                    cpy_f32_q4_1,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,                    cpy_f32_q5_0,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,                    cpy_f32_q5_1,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,                  cpy_f32_iq4_nl,                  true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q4_0_F32,                    cpy_q4_0_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q4_0_F16,                    cpy_q4_0_f16,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q4_1_F32,                    cpy_q4_1_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q4_1_F16,                    cpy_q4_1_f16,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q5_0_F32,                    cpy_q5_0_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q5_0_F16,                    cpy_q5_0_f16,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q5_1_F32,                    cpy_q5_1_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q5_1_F16,                    cpy_q5_1_f16,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q8_0_F32,                    cpy_q8_0_f32,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_Q8_0_F16,                    cpy_q8_0_f16,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT,                          concat,                          true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR,                             sqr,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQRT,                            sqrt,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN,                             sin,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS,                             cos,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NEG,                             neg,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REGLU,                           reglu,                           true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU,                           geglu,                           true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SWIGLU,                          swiglu,                          true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SWIGLU_OAI,                      swiglu_oai,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU_ERF,                       geglu_erf,                       true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU_QUICK,                     geglu_quick,                     true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                        sum_rows,                        true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MEAN,                            mean,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGMAX,                          argmax,                          true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,                 pool_2d_avg_f32,                 true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,                 pool_2d_max_f32,                 true);
    }

    return ctx;
 }

 static void ggml_metal_free(struct ggml_backend_metal_context * ctx) {
    GGML_LOG_INFO("%s: deallocating\n", __func__);

    for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) {
        [ctx->kernels[i].pipeline release];
    }

    Block_release(ctx->encode_async);

    [ctx->queue release];

    for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) {
        // ctx->cmd_bufs[i].obj is auto released

        ggml_metal_mem_pool_free(ctx->cmd_bufs[i].mem_pool);
    }

    dispatch_release(ctx->d_queue);

    free(ctx);
 }

 // temporarily defined here for compatibility between ggml-backend and the old API

 struct ggml_backend_metal_buffer {
    void   * data;
    size_t   size;

    id<MTLBuffer> metal;
 };

 struct ggml_backend_metal_buffer_context {
    void * all_data;
    size_t all_size;
    bool owned;

    // multiple buffers are used only to avoid the maximum buffer size limitation when using mmap
    int n_buffers;
    struct ggml_backend_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];

    // optional MTLResidencySet
    id rset;
 };

 // rset init
 static bool ggml_backend_metal_buffer_rset_init(
        struct ggml_backend_metal_buffer_context * ctx,
        struct ggml_backend_metal_device_context * ctx_dev,
        id<MTLDevice> device) {
    ctx->rset = nil;

    if (!ctx_dev->has_residency_sets) {
        return true;
    }

 #if defined(GGML_METAL_HAS_RESIDENCY_SETS)
    if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) {
        MTLResidencySetDescriptor * desc = [[MTLResidencySetDescriptor alloc] init];
        desc.label = @"ggml_backend_metal";
        desc.initialCapacity = ctx->n_buffers;

        NSError * error;
        ctx->rset = [device newResidencySetWithDescriptor:desc error:&error];
        if (error) {
            GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
            [desc release];
            return false;
        }

        [desc release];

        for (int i = 0; i < ctx->n_buffers; i++) {
            [ctx->rset addAllocation:ctx->buffers[i].metal];
        }

        [ctx->rset commit];
        [ctx->rset requestResidency];

        return true;
    }
 #else
    GGML_UNUSED(ctx_dev);
    GGML_UNUSED(device);
 #endif

    return true;
 }

 // rset free
 static void ggml_backend_metal_buffer_rset_free(struct ggml_backend_metal_buffer_context * ctx) {
 #if defined(GGML_METAL_HAS_RESIDENCY_SETS)
    if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) {
        if (ctx->rset) {
            [ctx->rset endResidency];
            [ctx->rset removeAllAllocations];
            [ctx->rset release];
        }
    }
 #else
    GGML_UNUSED(ctx);
 #endif
 }

 // finds the Metal buffer that contains the tensor data on the GPU device
 // the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
 // Metal buffer based on the host memory pointer
 //
 static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs) {
    //GGML_LOG_INFO("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);

    const int64_t tsize = ggml_nbytes(t);

    ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer;

    struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context;

    // find the view that contains the tensor fully
    for (int i = 0; i < buf_ctx->n_buffers; ++i) {
        const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data;

        //GGML_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size);
        if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) {
            *offs = (size_t) ioffs;

            //GGML_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs);

            return buf_ctx->buffers[i].metal;
        }
    }

    GGML_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name);

    return nil;
 }

 static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_context * ctx_dev, const struct ggml_tensor * op) {
    const bool has_simdgroup_mm        = ctx_dev->has_simdgroup_mm;
    const bool has_simdgroup_reduction = ctx_dev->has_simdgroup_reduction;
    const bool use_bfloat              = ctx_dev->use_bfloat;

    if (!use_bfloat) {
        if (op->type == GGML_TYPE_BF16) {
            return false;
        }

        for (size_t i = 0, n = 3; i < n; ++i) {
            if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) {
                return false;
            }
        }
    }

    switch (op->op) {
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(op)) {
                case GGML_UNARY_OP_TANH:
                case GGML_UNARY_OP_RELU:
                case GGML_UNARY_OP_SIGMOID:
                case GGML_UNARY_OP_GELU:
                case GGML_UNARY_OP_GELU_ERF:
                case GGML_UNARY_OP_GELU_QUICK:
                case GGML_UNARY_OP_SILU:
                case GGML_UNARY_OP_ELU:
                case GGML_UNARY_OP_NEG:
                case GGML_UNARY_OP_ABS:
                case GGML_UNARY_OP_SGN:
                case GGML_UNARY_OP_STEP:
                case GGML_UNARY_OP_HARDSWISH:
                case GGML_UNARY_OP_HARDSIGMOID:
                case GGML_UNARY_OP_EXP:
                    return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
                default:
                    return false;
            }
        case GGML_OP_GLU:
            switch (ggml_get_glu_op(op)) {
                case GGML_GLU_OP_REGLU:
                case GGML_GLU_OP_GEGLU:
                case GGML_GLU_OP_SWIGLU:
                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
                    return ggml_is_contiguous_1(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
               default:
                    return false;
            }
        case GGML_OP_NONE:
        case GGML_OP_RESHAPE:
        case GGML_OP_VIEW:
        case GGML_OP_TRANSPOSE:
        case GGML_OP_PERMUTE:
        case GGML_OP_CONCAT:
            return true;
        case GGML_OP_ADD:
        case GGML_OP_SUB:
        case GGML_OP_MUL:
        case GGML_OP_DIV:
        case GGML_OP_ADD_ID:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_ACC:
        case GGML_OP_REPEAT:
        case GGML_OP_SCALE:
        case GGML_OP_CONV_TRANSPOSE_1D:
            return true;
        case GGML_OP_CLAMP:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_SQR:
        case GGML_OP_SQRT:
        case GGML_OP_SIN:
        case GGML_OP_COS:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_LOG:
            return false; // TODO: implement
        case GGML_OP_SUM_ROWS:
        case GGML_OP_MEAN:
        case GGML_OP_SOFT_MAX:
        case GGML_OP_GROUP_NORM:
            return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
        case GGML_OP_RMS_NORM:
        case GGML_OP_L2_NORM:
            return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
        case GGML_OP_ARGMAX:
            return true;
        case GGML_OP_NORM:
            return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
        case GGML_OP_ROPE:
            return true;
        case GGML_OP_IM2COL:
            return op->src[0]->type == GGML_TYPE_F16;
        case GGML_OP_POOL_1D:
            return false;
        case GGML_OP_UPSCALE:
            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
        case GGML_OP_POOL_2D:
        case GGML_OP_PAD:
        case GGML_OP_PAD_REFLECT_1D:
        case GGML_OP_TIMESTEP_EMBEDDING:
        case GGML_OP_ARGSORT:
        case GGML_OP_LEAKY_RELU:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_ARANGE:
            return true;
        case GGML_OP_FLASH_ATTN_EXT:
            if (op->src[0]->ne[0] == 32) {
                // head size == 32 (e.g. bert-bge-small)
                // TODO: not sure if it is worth adding kernels for this size
                return false;
            }
            if (op->src[0]->ne[0] == 576) {
                // DeepSeek sizes
                // TODO: disabled for now, until optmized
                return false;
            }
            if (op->src[1]->type != op->src[2]->type) {
                return false;
            }
            return has_simdgroup_mm; // TODO: over-restricted for vec-kernels
        case GGML_OP_SSM_CONV:
        case GGML_OP_SSM_SCAN:
        case GGML_OP_RWKV_WKV6:
        case GGML_OP_RWKV_WKV7:
            return true;
        case GGML_OP_MUL_MAT:
        case GGML_OP_MUL_MAT_ID:
            return has_simdgroup_reduction &&
                (op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F32);
        case GGML_OP_CPY:
        case GGML_OP_DUP:
        case GGML_OP_CONT:
            {
                switch (op->src[0]->type) {
                    case GGML_TYPE_F32:
                        switch (op->type) {
                           case GGML_TYPE_F32:
                           case GGML_TYPE_F16:
                           case GGML_TYPE_BF16:
                           case GGML_TYPE_Q8_0:
                           case GGML_TYPE_Q4_0:
                           case GGML_TYPE_Q4_1:
                           case GGML_TYPE_Q5_0:
                           case GGML_TYPE_Q5_1:
                           case GGML_TYPE_IQ4_NL:
                                return true;
                           default:
                                return false;
                        }
                    case GGML_TYPE_F16:
                        switch (op->type) {
                            case GGML_TYPE_F32:
                            case GGML_TYPE_F16:
                                return true;
                            default:
                                return false;
                        }
                    case GGML_TYPE_BF16:
                        switch (op->type) {
                            case GGML_TYPE_F32:
                            case GGML_TYPE_BF16:
                                return true;
                            default:
                                return false;
                        }
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q5_1:
                    case GGML_TYPE_Q8_0:
                        switch (op->type) {
                            case GGML_TYPE_F32:
                            case GGML_TYPE_F16:
                                return true;
                            default:
                                return false;
                        }
                    default:
                        return false;
                };
            }
        case GGML_OP_SET:
            {
                switch (op->src[0]->type) {
                    case GGML_TYPE_F32:
                    case GGML_TYPE_I32:
                        return true;
                    default:
                        return false;
                };
            }
        case GGML_OP_DIAG_MASK_INF:
        case GGML_OP_GET_ROWS:
            {
                return op->ne[3] == 1;
            }
        case GGML_OP_SET_ROWS:
            {
                if (op->src[0]->type != GGML_TYPE_F32) {
                    return false;
                }

                switch (op->type) {
                    case GGML_TYPE_F32:
                    case GGML_TYPE_F16:
                    case GGML_TYPE_BF16:
                    case GGML_TYPE_Q8_0:
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q5_1:
                    case GGML_TYPE_IQ4_NL:
                        return true;
                    default:
                        return false;
                };
            }
        default:
            return false;
    }
 }

 static int ggml_metal_encode_node(
                        ggml_backend_t   backend,
                                   int   idx,
                                   int   idx_end,
          id<MTLComputeCommandEncoder>   encoder,
            struct ggml_metal_mem_pool * mem_pool) {
    struct ggml_backend_metal_context        * ctx     = backend->context;
    struct ggml_backend_metal_device_context * ctx_dev = backend->device->context;

    struct ggml_cgraph * gf = ctx->gf;

    enum ggml_op ops[8];

    struct ggml_tensor ** nodes = ggml_graph_nodes(gf) + idx;
    struct ggml_tensor *  node  = nodes[0];

    //GGML_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, idx, ggml_op_name(node->op));

    struct ggml_tensor * src0 = node->src[0];
    struct ggml_tensor * src1 = node->src[1];
    struct ggml_tensor * src2 = node->src[2];
    struct ggml_tensor * dst  = node;

    if (ggml_is_empty(dst)) {
        return 1;
    }

    switch (dst->op) {
        case GGML_OP_NONE:
        case GGML_OP_RESHAPE:
        case GGML_OP_VIEW:
        case GGML_OP_TRANSPOSE:
        case GGML_OP_PERMUTE:
            {
                // noop -> next node
            } return 1;
        default:
            {
            } break;
    }

    if (!ggml_metal_supports_op(ctx_dev, dst)) {
        GGML_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
        GGML_ABORT("unsupported op");
    }

    ggml_metal_mem_pool_clear(mem_pool);

    const int64_t  ne00 = src0 ? src0->ne[0] : 0;
    const int64_t  ne01 = src0 ? src0->ne[1] : 0;
    const int64_t  ne02 = src0 ? src0->ne[2] : 0;
    const int64_t  ne03 = src0 ? src0->ne[3] : 0;

    const uint64_t nb00 = src0 ? src0->nb[0] : 0;
    const uint64_t nb01 = src0 ? src0->nb[1] : 0;
    const uint64_t nb02 = src0 ? src0->nb[2] : 0;
    const uint64_t nb03 = src0 ? src0->nb[3] : 0;

    const int64_t  ne10 = src1 ? src1->ne[0] : 0;
    const int64_t  ne11 = src1 ? src1->ne[1] : 0;
    const int64_t  ne12 = src1 ? src1->ne[2] : 0;
    const int64_t  ne13 = src1 ? src1->ne[3] : 0;

    const uint64_t nb10 = src1 ? src1->nb[0] : 0;
    const uint64_t nb11 = src1 ? src1->nb[1] : 0;
    const uint64_t nb12 = src1 ? src1->nb[2] : 0;
    const uint64_t nb13 = src1 ? src1->nb[3] : 0;

    const int64_t  ne20 = src2 ? src2->ne[0] : 0;
    const int64_t  ne21 = src2 ? src2->ne[1] : 0;
    const int64_t  ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22);
    const int64_t  ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23);

    const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20);
    const uint64_t nb21 = src2 ? src2->nb[1] : 0;
    const uint64_t nb22 = src2 ? src2->nb[2] : 0;
    const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23);

    const int64_t  ne0  =  dst ?  dst->ne[0] : 0;
    const int64_t  ne1  =  dst ?  dst->ne[1] : 0;
    const int64_t  ne2  =  dst ?  dst->ne[2] : 0;
    const int64_t  ne3  =  dst ?  dst->ne[3] : 0;

    const uint64_t nb0  =  dst ?  dst->nb[0] : 0;
    const uint64_t nb1  =  dst ?  dst->nb[1] : 0;
    const uint64_t nb2  =  dst ?  dst->nb[2] : 0;
    const uint64_t nb3  =  dst ?  dst->nb[3] : 0;

    const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
    const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
    const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT;
    const enum ggml_type dstt  = dst  ? dst->type  : GGML_TYPE_COUNT;

    size_t offs_src0 = 0;
    size_t offs_src1 = 0;
    size_t offs_src2 = 0;
    size_t offs_dst  = 0;

    id<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(src0, &offs_src0) : nil;
    id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(src1, &offs_src1) : nil;
    id<MTLBuffer> id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil;
    id<MTLBuffer> id_dst  = dst  ? ggml_metal_get_buffer(dst,  &offs_dst)  : nil;

    int n_fuse = 1;

 #if 0
    GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op));
    if (src0) {
        GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03,
                ggml_is_contiguous(src0), src0->name);
    }
    if (src1) {
        GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13,
                ggml_is_contiguous(src1), src1->name);
    }
    if (dst) {
        GGML_LOG_INFO("%s: dst  - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, ne3, nb0, nb1, nb2, nb3,
                dst->name);
    }
 #endif

    id<MTLDevice> device = ctx_dev->mtl_device;

    switch (dst->op) {
        case GGML_OP_CONCAT:
            {
                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline;

                const int32_t dim = ((const int32_t *) dst->op_params)[0];

                ggml_metal_kargs_concat args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne10 =*/ ne10,
                    /*.ne11 =*/ ne11,
                    /*.ne12 =*/ ne12,
                    /*.ne13 =*/ ne13,
                    /*.nb10 =*/ nb10,
                    /*.nb11 =*/ nb11,
                    /*.nb12 =*/ nb12,
                    /*.nb13 =*/ nb13,
                    /*.ne0  =*/ ne0,
                    /*.ne1  =*/ ne1,
                    /*.ne2  =*/ ne2,
                    /*.ne3  =*/ ne3,
                    /*.nb0  =*/ nb0,
                    /*.nb1  =*/ nb1,
                    /*.nb2  =*/ nb2,
                    /*.nb3  =*/ nb3,
                    /*.dim  =*/ dim,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:3];

                const int nth = MIN(1024, ne0);

                [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_ADD:
        case GGML_OP_SUB:
        case GGML_OP_MUL:
        case GGML_OP_DIV:
            {
                GGML_ASSERT(src0t == GGML_TYPE_F32);
                GGML_ASSERT(src1t == GGML_TYPE_F32);

                GGML_ASSERT(ggml_is_contiguous_rows(src0));
                GGML_ASSERT(ggml_is_contiguous_rows(src1));

                const size_t offs = 0;

                bool bcast_row = false;

                id<MTLComputePipelineState> pipeline = nil;

                ggml_metal_kargs_bin args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne10 =*/ ne10,
                    /*.ne11 =*/ ne11,
                    /*.ne12 =*/ ne12,
                    /*.ne13 =*/ ne13,
                    /*.nb10 =*/ nb10,
                    /*.nb11 =*/ nb11,
                    /*.nb12 =*/ nb12,
                    /*.nb13 =*/ nb13,
                    /*.ne0  =*/ ne0,
                    /*.ne1  =*/ ne1,
                    /*.ne2  =*/ ne2,
                    /*.ne3  =*/ ne3,
                    /*.nb0  =*/ nb0,
                    /*.nb1  =*/ nb1,
                    /*.nb2  =*/ nb2,
                    /*.nb3  =*/ nb3,
                    /*.offs =*/ offs,
                    /*.o1   =*/ { offs_src1 },
                };

                // c[0] = add(a,    b[0])
                // c[1] = add(c[0], b[1])
                // c[2] = add(c[1], b[2])
                // ...
                if (ctx_dev->use_fusion) {
                    ops[0] = GGML_OP_ADD;
                    ops[1] = GGML_OP_ADD;
                    ops[2] = GGML_OP_ADD;
                    ops[3] = GGML_OP_ADD;
                    ops[4] = GGML_OP_ADD;
                    ops[5] = GGML_OP_ADD;
                    ops[6] = GGML_OP_ADD;
                    ops[7] = GGML_OP_ADD;

                    size_t offs_fuse;
                    id<MTLBuffer> id_fuse;

                    // note: in metal, we sometimes encode the graph in parallel so we have to avoid fusing nodes
                    //       across splits. idx_end indicates the last node in the current split
                    for (n_fuse = 0; n_fuse <= 6 && idx + n_fuse + 1 < idx_end; ++n_fuse) {
                        if (!ggml_can_fuse(gf, idx + n_fuse, ops + n_fuse, 2)) {
                            break;
                        }

                        if (nodes[n_fuse] != nodes[n_fuse + 1]->src[0]) {
                            break;
                        }

                        // b[0] === b[1] === ...
                        if (!ggml_are_same_layout(nodes[n_fuse]->src[1], nodes[n_fuse + 1]->src[1])) {
                            break;
                        }

                        // only fuse nodes if src1 is in the same Metal buffer
                        id_fuse = ggml_metal_get_buffer(nodes[n_fuse + 1]->src[1], &offs_fuse);
                        if (id_fuse != id_src1) {
                            break;
                        }

                        ctx_dev->fuse_cnt[nodes[n_fuse + 1]->op]++;

                        args.o1[n_fuse + 1] = offs_fuse;
                    }

                    ++n_fuse;

                    if (ctx_dev->debug_fusion > 1 && n_fuse > 1) {
                        GGML_LOG_DEBUG("%s: fuse: ADD x %d\n", __func__, n_fuse);
                    }
                }

                if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
                    GGML_ASSERT(ggml_is_contiguous(src0));

                    // src1 is a row
                    GGML_ASSERT(ne11 == 1);

                    switch (dst->op) {
                        case GGML_OP_ADD:
                            {
                                switch (n_fuse) {
                                    case 1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW_C4       ].pipeline; break;
                                    case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_2].pipeline; break;
                                    case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_3].pipeline; break;
                                    case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_4].pipeline; break;
                                    case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_5].pipeline; break;
                                    case 6: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_6].pipeline; break;
                                    case 7: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_7].pipeline; break;
                                    case 8: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW_C4_FUSE_8].pipeline; break;
                                    default: GGML_ABORT("fatal error");
                                }
                            } break;
                        case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB_ROW_C4].pipeline; break;
                        case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW_C4].pipeline; break;
                        case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW_C4].pipeline; break;
                        default: GGML_ABORT("fatal error");
                    }

                    bcast_row = true;
                } else {
                    switch (dst->op) {
                        case GGML_OP_ADD:
                            {
                                switch (n_fuse) {
                                    case 1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD       ].pipeline; break;
                                    case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_FUSE_2].pipeline; break;
                                    case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_FUSE_3].pipeline; break;
                                    case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_FUSE_4].pipeline; break;
                                    case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_FUSE_5].pipeline; break;
                                    case 6: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_FUSE_6].pipeline; break;
                                    case 7: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_FUSE_7].pipeline; break;
                                    case 8: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_FUSE_8].pipeline; break;
                                    default: GGML_ABORT("fatal error");
                                }
                            } break;
                        case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB].pipeline; break;
                        case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break;
                        case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break;
                        default: GGML_ABORT("fatal error");
                    }
                }

                if (n_fuse > 1) {
                    id_dst = ggml_metal_get_buffer(nodes[n_fuse - 1], &offs_dst);
                }

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_src1 offset:0         atIndex:2];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:3];

                if (bcast_row) {
                    const int64_t n = ggml_nelements(dst)/4;

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } else {
                    int nth = 32;

                    while (16*nth < ne0 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
                        nth *= 2;
                    }

                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                }
            } break;
        case GGML_OP_ADD_ID:
            {
                GGML_ASSERT(src0t == GGML_TYPE_F32);
                GGML_ASSERT(src1t == GGML_TYPE_F32);
                GGML_ASSERT(src2t == GGML_TYPE_I32);
                GGML_ASSERT(dstt  == GGML_TYPE_F32);

                GGML_ASSERT(ggml_is_contiguous_rows(src0));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ID].pipeline;

                ggml_metal_kargs_add_id args = {
                    /*.ne0  =*/ ne0,
                    /*.ne1  =*/ ne1,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb11 =*/ nb11,
                    /*.nb21 =*/ nb21,

                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
                [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:4];

                const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00);

                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_REPEAT:
            {
                id<MTLComputePipelineState> pipeline;

                switch (src0t) {
                    case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break;
                    case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break;
                    case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break;
                    case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break;
                    default: GGML_ABORT("fatal error");
                }

                ggml_metal_kargs_repeat args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne0  =*/ ne0,
                    /*.ne1  =*/ ne1,
                    /*.ne2  =*/ ne2,
                    /*.ne3  =*/ ne3,
                    /*.nb0  =*/ nb0,
                    /*.nb1  =*/ nb1,
                    /*.nb2  =*/ nb2,
                    /*.nb3  =*/ nb3,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];

                const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);

                [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_ACC:
            {
                GGML_ASSERT(src0t == GGML_TYPE_F32);
                GGML_ASSERT(src1t == GGML_TYPE_F32);
                GGML_ASSERT(dstt  == GGML_TYPE_F32);

                GGML_ASSERT(ggml_is_contiguous(src0));
                GGML_ASSERT(ggml_is_contiguous(src1));

                const size_t pnb1 = ((const int32_t *) dst->op_params)[0];
                const size_t pnb2 = ((const int32_t *) dst->op_params)[1];
                const size_t pnb3 = ((const int32_t *) dst->op_params)[2];
                const size_t offs = ((const int32_t *) dst->op_params)[3];

                const bool inplace = (bool) ((const int32_t *) dst->op_params)[4];

                if (!inplace) {
                    // run a separete kernel to cpy src->dst
                    // not sure how to avoid this
                    // TODO: make a simpler cpy_bytes kernel

                    const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline;

                    ggml_metal_kargs_cpy args = {
                        /*.ne00 =*/ ne00,
                        /*.ne01 =*/ ne01,
                        /*.ne02 =*/ ne02,
                        /*.ne03 =*/ ne03,
                        /*.nb00 =*/ nb00,
                        /*.nb01 =*/ nb01,
                        /*.nb02 =*/ nb02,
                        /*.nb03 =*/ nb03,
                        /*.ne0  =*/ ne0,
                        /*.ne1  =*/ ne1,
                        /*.ne2  =*/ ne2,
                        /*.ne3  =*/ ne3,
                        /*.nb0  =*/ nb0,
                        /*.nb1  =*/ nb1,
                        /*.nb2  =*/ nb2,
                        /*.nb3  =*/ nb3,
                    };

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBytes:&args length:sizeof(args) atIndex:0];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];

                    const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00);

                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                }

                const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline;

                ggml_metal_kargs_bin args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ pnb1,
                    /*.nb02 =*/ pnb2,
                    /*.nb03 =*/ pnb3,
                    /*.ne10 =*/ ne10,
                    /*.ne11 =*/ ne11,
                    /*.ne12 =*/ ne12,
                    /*.ne13 =*/ ne13,
                    /*.nb10 =*/ nb10,
                    /*.nb11 =*/ nb11,
                    /*.nb12 =*/ nb12,
                    /*.nb13 =*/ nb13,
                    /*.ne0  =*/ ne0,
                    /*.ne1  =*/ ne1,
                    /*.ne2  =*/ ne2,
                    /*.ne3  =*/ ne3,
                    /*.nb0  =*/ nb0,
                    /*.nb1  =*/ pnb1,
                    /*.nb2  =*/ pnb2,
                    /*.nb3  =*/ pnb3,
                    /*.offs =*/ offs,
                    /*.o1   =*/ { offs_src1},
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_src1 offset:0         atIndex:2];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:3];

                const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00);

                [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_SCALE:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));

                float scale;
                float bias;
                memcpy(&scale, ((const int32_t *) dst->op_params) + 0, sizeof(float));
                memcpy(&bias,  ((const int32_t *) dst->op_params) + 1, sizeof(float));

                int64_t n = ggml_nelements(dst);

                id<MTLComputePipelineState> pipeline = nil;

                if (n % 4 == 0) {
                    n /= 4;
                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline;
                } else {
                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline;
                }

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0   offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst    offset:offs_dst  atIndex:1];
                [encoder setBytes:&scale length:sizeof(scale) atIndex:2];
                [encoder setBytes:&bias  length:sizeof(bias)  atIndex:3];

                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_CLAMP:
            {
                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline;

                float min;
                float max;
                memcpy(&min, ((const int32_t *) dst->op_params) + 0, sizeof(float));
                memcpy(&max, ((const int32_t *) dst->op_params) + 1, sizeof(float));

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                [encoder setBytes:&min   length:sizeof(min) atIndex:2];
                [encoder setBytes:&max   length:sizeof(max) atIndex:3];

                const int64_t n = ggml_nelements(dst);

                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(node)) {
                // we are not taking into account the strides, so for now require contiguous tensors
                GGML_ASSERT(ggml_is_contiguous(src0));

                case GGML_UNARY_OP_TANH:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_RELU:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_SIGMOID:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_GELU:
                {
                    int64_t n = ggml_nelements(dst);

                    id<MTLComputePipelineState> pipeline = nil;

                    if (n % 4 == 0) {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline;
                        n /= 4;
                    } else {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline;
                    }

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_GELU_ERF:
                {
                    int64_t n = ggml_nelements(dst);

                    id<MTLComputePipelineState> pipeline = nil;

                    if (n % 4 == 0) {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_ERF_4].pipeline;
                        n /= 4;
                    } else {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_ERF].pipeline;
                    }

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_GELU_QUICK:
                {
                    int64_t n = ggml_nelements(dst);

                    id<MTLComputePipelineState> pipeline = nil;

                    if (n % 4 == 0) {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline;
                        n /= 4;
                    } else {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline;
                    }

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_SILU:
                {
                    int64_t n = ggml_nelements(dst);

                    id<MTLComputePipelineState> pipeline = nil;

                    if (n % 4 == 0) {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline;
                        n /= 4;
                    } else {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline;
                    }

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_ELU:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ELU].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_NEG:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NEG].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_ABS:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ABS].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_SGN:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SGN].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_STEP:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_STEP].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_HARDSWISH:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_HARDSWISH].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_HARDSIGMOID:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_HARDSIGMOID].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                case GGML_UNARY_OP_EXP:
                {
                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_EXP].pipeline;

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];

                    const int64_t n = ggml_nelements(dst);

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
                default:
                {
                    GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));
                    GGML_ABORT("fatal error");
                }
            } break;
        case GGML_OP_GLU:
            {
                GGML_ASSERT(ggml_is_contiguous_1(src0));

                if (src1) {
                    GGML_ASSERT(ggml_are_same_shape(src0, src1));
                }

                id<MTLComputePipelineState> pipeline = nil;

                switch (ggml_get_glu_op(node)) {
                    case GGML_GLU_OP_REGLU:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REGLU].pipeline;
                        break;
                    case GGML_GLU_OP_GEGLU:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GEGLU].pipeline;
                        break;
                    case GGML_GLU_OP_SWIGLU:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SWIGLU].pipeline;
                        break;
                    case GGML_GLU_OP_SWIGLU_OAI:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SWIGLU_OAI].pipeline;
                        break;
                    case GGML_GLU_OP_GEGLU_ERF:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GEGLU_ERF].pipeline;
                        break;
                    case GGML_GLU_OP_GEGLU_QUICK:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GEGLU_QUICK].pipeline;
                        break;
                    default:
                        GGML_ABORT("fatal error");
                }

                const int32_t swp = ggml_get_op_params_i32(dst, 1);
                const float alpha = ggml_get_op_params_f32(dst, 2);
                const float limit = ggml_get_op_params_f32(dst, 3);

                const int32_t i00 = swp ? ne0 : 0;
                const int32_t i10 = swp ? 0 : ne0;

                ggml_metal_kargs_glu args = {
                    /*.ne00 =*/ ne00,
                    /*.nb01 =*/ nb01,
                    /*.ne10 =*/ src1 ? ne10 : ne00,
                    /*.nb11 =*/ src1 ? nb11 : nb01,
                    /*.ne0  =*/ ne0,
                    /*.nb1  =*/ nb1,
                    /*.i00  =*/ src1 ? 0 : i00,
                    /*.i10  =*/ src1 ? 0 : i10,
                    /*.alpha=*/ alpha,
                    /*.limit=*/ limit
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                if (src1) {
                    [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                } else {
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                }
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
                [encoder setBytes:&args length:sizeof(args) atIndex:3];

                const int64_t nrows = ggml_nrows(src0);

                const int32_t nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00/2);

                [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_SQR:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline;

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst atIndex:1];

                const int64_t n = ggml_nelements(dst);

                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_SQRT:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQRT].pipeline;

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst atIndex:1];

                const int64_t n = ggml_nelements(dst);

                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_SIN:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIN].pipeline;

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst atIndex:1];

                const int64_t n = ggml_nelements(dst);

                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_COS:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_COS].pipeline;

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst atIndex:1];

                const int64_t n = ggml_nelements(dst);

                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_SUM_ROWS:
        case GGML_OP_MEAN:
            {
                GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));

                id<MTLComputePipelineState> pipeline = nil;

                switch (dst->op) {
                    case GGML_OP_SUM_ROWS:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
                        break;
                    case GGML_OP_MEAN:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MEAN].pipeline;
                        break;
                    default:
                        GGML_ABORT("fatal error");
                }

                int nth = 32; // SIMD width

                while (nth < ne00 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
                    nth *= 2;
                }

                nth = MIN(nth, (int) pipeline.maxTotalThreadsPerThreadgroup);
                nth = MIN(nth, ne00);

                ggml_metal_kargs_sum_rows args = {
                   /*.ne00 =*/ ne00,
                   /*.ne01 =*/ ne01,
                   /*.ne02 =*/ ne02,
                   /*.ne03 =*/ ne03,
                   /*.nb00 =*/ nb00,
                   /*.nb01 =*/ nb01,
                   /*.nb02 =*/ nb02,
                   /*.nb03 =*/ nb03,
                   /*.ne10 =*/ ne10,
                   /*.ne11 =*/ ne11,
                   /*.ne12 =*/ ne12,
                   /*.ne13 =*/ ne13,
                   /*.nb10 =*/ nb10,
                   /*.nb11 =*/ nb11,
                   /*.nb12 =*/ nb12,
                   /*.nb13 =*/ nb13,
                   /*.ne0  =*/ ne0,
                   /*.ne1  =*/ ne1,
                   /*.ne2  =*/ ne2,
                   /*.ne3  =*/ ne3,
                   /*.nb0  =*/ nb0,
                   /*.nb1  =*/ nb1,
                   /*.nb2  =*/ nb2,
                   /*.nb3  =*/ nb3,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];

                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_SOFT_MAX:
            {
                GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32);

                int nth = 32; // SIMD width

                id<MTLComputePipelineState> pipeline = nil;

                const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);

                if (ne00%4 == 0) {
                    while (nth < ne00/4 && nth*ne01*ne02*ne03 < 256) {
                        nth *= 2;
                    }
                    if (use_f16) {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline;
                    } else {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline;
                    }
                } else {
                    while (nth < ne00 && nth*ne01*ne02*ne03 < 256) {
                        nth *= 2;
                    }
                    if (use_f16) {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline;
                    } else {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline;
                    }
                }

                float scale;
                float max_bias;

                memcpy(&scale,    ((const int32_t *) dst->op_params) + 0, sizeof(scale));
                memcpy(&max_bias, ((const int32_t *) dst->op_params) + 1, sizeof(max_bias));

                const uint32_t n_head      = src0->ne[2];
                const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));

                const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
                const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

 // use this branch to test the ggml_metal_mem_pool functionality
 #if 0
                // cpy to tmp buffer in MTLHeap

                id<MTLBuffer> h_src0 = h_src0 = ggml_metal_mem_pool_alloc(mem_pool, ggml_nbytes(src0));
                if (!h_src0) {
                    GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, ggml_nbytes(src0));
                    return 0;
                }

                offs_src0 = 0;

                ggml_metal_kargs_cpy args_cpy = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne0  =*/ ne00,
                    /*.ne1  =*/ ne01,
                    /*.ne2  =*/ ne02,
                    /*.ne3  =*/ ne03,
                    /*.nb0  =*/ nb00,
                    /*.nb1  =*/ nb01,
                    /*.nb2  =*/ nb02,
                    /*.nb3  =*/ nb03,
                };

                if (src0->type == GGML_TYPE_F16) {
                    [encoder setComputePipelineState:ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline];
                } else {
                    [encoder setComputePipelineState:ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline];
                }
                [encoder setBytes:&args_cpy length:sizeof(args_cpy) atIndex:0];
                [encoder setBuffer:id_src0  offset:offs_src0        atIndex:1];
                [encoder setBuffer:h_src0   offset:0                atIndex:2];

                GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
                int nth_cpy = MIN(1024, ne00 / ggml_blck_size(src0->type));

                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth_cpy, 1, 1)];

 #else
                id<MTLBuffer> h_src0 = id_src0;
 #endif
                // softmax

                ggml_metal_kargs_soft_max args = {
                    /*.ne00        =*/ ne00,
                    /*.ne01        =*/ ne01,
                    /*.ne02        =*/ ne02,
                    /*.nb01        =*/ nb01,
                    /*.nb02        =*/ nb02,
                    /*.nb03        =*/ nb03,
                    /*.ne11        =*/ ne11,
                    /*.ne12        =*/ ne12,
                    /*.ne13        =*/ ne13,
                    /*.nb11        =*/ nb11,
                    /*.nb12        =*/ nb12,
                    /*.nb13        =*/ nb13,
                    /*.nb1         =*/ nb1,
                    /*.nb2         =*/ nb2,
                    /*.nb3         =*/ nb3,
                    /*.scale       =*/ scale,
                    /*.max_bias    =*/ max_bias,
                    /*.m0          =*/ m0,
                    /*.m1          =*/ m1,
                    /*.n_head_log2 =*/ n_head_log2,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:h_src0 offset:offs_src0      atIndex:0];
                if (id_src1) {
                    [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                } else {
                    [encoder setBuffer:h_src0 offset:offs_src0  atIndex:1];
                }
                if (id_src2) {
                    [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
                } else {
                    [encoder setBuffer:h_src0 offset:offs_src0  atIndex:2];
                }
                [encoder setBuffer:id_dst offset:offs_dst       atIndex:3];
                [encoder setBytes:&args   length:sizeof(args)   atIndex:4];

                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];

                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_DIAG_MASK_INF:
            {
                const int n_past = ((const int32_t *)(dst->op_params))[0];

                id<MTLComputePipelineState> pipeline = nil;

                if (ne00%8 == 0) {
                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline;
                } else {
                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline;
                }

                ggml_metal_kargs_diag_mask_inf args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.n_past =*/ n_past,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                [encoder setBytes:&args  length:sizeof(args) atIndex:2];

                if (ne00%8 == 0) {
                    [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                }
                else {
                    [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                }
            } break;
        case GGML_OP_SSM_CONV:
            {
                GGML_ASSERT(src0t == GGML_TYPE_F32);
                GGML_ASSERT(src1t == GGML_TYPE_F32);

                GGML_ASSERT(ggml_is_contiguous(src0));
                GGML_ASSERT(ggml_is_contiguous(src1));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline;

                ggml_metal_kargs_ssm_conv args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.ne10 =*/ ne10,
                    /*.ne11 =*/ ne11,
                    /*.nb10 =*/ nb10,
                    /*.nb11 =*/ nb11,
                    /*.ne0  =*/ ne0,
                    /*.ne1  =*/ ne1,
                    /*.ne2  =*/ ne2,
                    /*.nb0  =*/ nb0,
                    /*.nb1  =*/ nb1,
                    /*.nb2  =*/ nb2,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0    atIndex:0];
                [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
                [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
                [encoder setBytes:&args    length:sizeof(args) atIndex:3];

                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne1, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_SSM_SCAN:
            {
                struct ggml_tensor * src3 = node->src[3];
                struct ggml_tensor * src4 = node->src[4];
                struct ggml_tensor * src5 = node->src[5];
                struct ggml_tensor * src6 = node->src[6];

                GGML_ASSERT(src3);
                GGML_ASSERT(src4);
                GGML_ASSERT(src5);
                GGML_ASSERT(src6);

                size_t offs_src3 = 0;
                size_t offs_src4 = 0;
                size_t offs_src5 = 0;
                size_t offs_src6 = 0;

                id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
                id<MTLBuffer> id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil;
                id<MTLBuffer> id_src5 = src5 ? ggml_metal_get_buffer(src5, &offs_src5) : nil;
                id<MTLBuffer> id_src6 = src6 ? ggml_metal_get_buffer(src6, &offs_src6) : nil;

                const int64_t  ne30 = src3->ne[0];
                const int64_t  ne31 = src3->ne[1]; GGML_UNUSED(ne31);

                const uint64_t nb30 = src3->nb[0]; GGML_UNUSED(nb30);
                const uint64_t nb31 = src3->nb[1];

                const int64_t  ne40 = src4->ne[0]; GGML_UNUSED(ne40);
                const int64_t  ne41 = src4->ne[1];
                const int64_t  ne42 = src4->ne[2]; GGML_UNUSED(ne42);
                const int64_t  ne43 = src4->ne[3]; GGML_UNUSED(ne43);

                const uint64_t nb40 = src4->nb[0]; GGML_UNUSED(nb40);
                const uint64_t nb41 = src4->nb[1];
                const uint64_t nb42 = src4->nb[2];
                const uint64_t nb43 = src4->nb[3];

                const int64_t  ne50 = src5->ne[0]; GGML_UNUSED(ne50);
                const int64_t  ne51 = src5->ne[1]; GGML_UNUSED(ne51);
                const int64_t  ne52 = src5->ne[2]; GGML_UNUSED(ne52);
                const int64_t  ne53 = src5->ne[3]; GGML_UNUSED(ne53);

                const uint64_t nb50 = src5->nb[0]; GGML_UNUSED(nb50);
                const uint64_t nb51 = src5->nb[1];
                const uint64_t nb52 = src5->nb[2];
                const uint64_t nb53 = src5->nb[3];

                const int64_t  ne60 = src6->ne[0]; GGML_UNUSED(ne60);

                const uint64_t nb60 = src6->nb[0]; GGML_UNUSED(nb60);

                const int64_t d_state      = ne00;
                const int64_t d_inner      = ne01;
                const int64_t n_head       = ne02;
                const int64_t n_group      = ne41;
                const int64_t n_seq_tokens = ne12;
                const int64_t n_seqs       = ne13;

                id<MTLComputePipelineState> pipeline = nil;

                if (ne30 == 1) {
                    // Mamba-2
                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32_GROUP].pipeline;
                } else {
                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline;
                }

                ggml_metal_kargs_ssm_scan args = {
                    /*.d_state      =*/ d_state,
                    /*.d_inner      =*/ d_inner,
                    /*.n_head       =*/ n_head,
                    /*.n_group      =*/ n_group,
                    /*.n_seq_tokens =*/ n_seq_tokens,
                    /*.n_seqs       =*/ n_seqs,
                    /*.s_off        =*/ ggml_nelements(src1) * sizeof(float),
                    /*.nb01         =*/ nb01,
                    /*.nb02         =*/ nb02,
                    /*.nb03         =*/ nb03,
                    /*.nb11         =*/ nb11,
                    /*.nb12         =*/ nb12,
                    /*.nb13         =*/ nb13,
                    /*.nb21         =*/ nb21,
                    /*.nb22         =*/ nb22,
                    /*.nb31         =*/ nb31,
                    /*.nb41         =*/ nb41,
                    /*.nb42         =*/ nb42,
                    /*.nb43         =*/ nb43,
                    /*.nb51         =*/ nb51,
                    /*.nb52         =*/ nb52,
                    /*.nb53         =*/ nb53,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
                [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3];
                [encoder setBuffer:id_src4 offset:offs_src4 atIndex:4];
                [encoder setBuffer:id_src5 offset:offs_src5 atIndex:5];
                [encoder setBuffer:id_src6 offset:offs_src6 atIndex:6];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:7];
                [encoder setBytes:&args    length:sizeof(args) atIndex:8];

                // One shared memory bucket for each simd group in the threadgroup
                // NOTE: Metal kernels require the buffer size to be multiple of 16 bytes
                //  https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength
                if (d_state >= 32) {
                    GGML_ASSERT((int64_t)(d_state / 32) <= 32);
                    const int64_t shmem_size = 32;
                    GGML_ASSERT(d_state <= (int64_t)pipeline.maxTotalThreadsPerThreadgroup);
                    [encoder setThreadgroupMemoryLength:(shmem_size)*sizeof(float) atIndex:0];
                }

                if (ne30 == 1) {
                    // Mamba-2
                    [encoder dispatchThreadgroups:MTLSizeMake(d_inner, n_head, n_seqs) threadsPerThreadgroup:MTLSizeMake(d_state, 1, 1)];
                } else {
                    GGML_ASSERT(d_inner == 1);
                    [encoder dispatchThreadgroups:MTLSizeMake(n_head, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(d_state, 1, 1)];
                }
            } break;
        case GGML_OP_RWKV_WKV6:
            {
                const int64_t B = dst->src[5]->ne[1];
                const int64_t T = dst->src[0]->ne[2];
                const int64_t C = dst->ne[0];
                const int64_t H = dst->src[0]->ne[1];

                GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32);
                GGML_ASSERT(C % H == 0);
                GGML_ASSERT(C / H == 64);

                size_t offs_src3 = 0;
                size_t offs_src4 = 0;
                size_t offs_src5 = 0;

                id<MTLBuffer> id_src3 = dst->src[3] ? ggml_metal_get_buffer(dst->src[3], &offs_src3) : nil;
                id<MTLBuffer> id_src4 = dst->src[4] ? ggml_metal_get_buffer(dst->src[4], &offs_src4) : nil;
                id<MTLBuffer> id_src5 = dst->src[5] ? ggml_metal_get_buffer(dst->src[5], &offs_src5) : nil;

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RWKV_WKV6_F32].pipeline;

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
                [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3];
                [encoder setBuffer:id_src4 offset:offs_src4 atIndex:4];
                [encoder setBuffer:id_src5 offset:offs_src5 atIndex:5];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:6];

                [encoder setBytes:&B length:sizeof(B) atIndex:7];
                [encoder setBytes:&T length:sizeof(T) atIndex:8];
                [encoder setBytes:&C length:sizeof(C) atIndex:9];
                [encoder setBytes:&H length:sizeof(H) atIndex:10];

                [encoder dispatchThreadgroups:MTLSizeMake(B * H, 1, 1) threadsPerThreadgroup:MTLSizeMake(C/ H, 1, 1)];
            } break;
        case GGML_OP_RWKV_WKV7:
            {
                const int64_t B = dst->src[6]->ne[1];
                const int64_t T = dst->src[0]->ne[2];
                const int64_t C = dst->ne[0];
                const int64_t H = dst->src[0]->ne[1];

                GGML_ASSERT(dst->src[6]->type == GGML_TYPE_F32);
                GGML_ASSERT(C % H == 0);
                GGML_ASSERT(C / H == 64);

                size_t offs_src3 = 0;
                size_t offs_src4 = 0;
                size_t offs_src5 = 0;
                size_t offs_src6 = 0;

                id<MTLBuffer> id_src3 = dst->src[3] ? ggml_metal_get_buffer(dst->src[3], &offs_src3) : nil;
                id<MTLBuffer> id_src4 = dst->src[4] ? ggml_metal_get_buffer(dst->src[4], &offs_src4) : nil;
                id<MTLBuffer> id_src5 = dst->src[5] ? ggml_metal_get_buffer(dst->src[5], &offs_src5) : nil;
                id<MTLBuffer> id_src6 = dst->src[6] ? ggml_metal_get_buffer(dst->src[6], &offs_src6) : nil;

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RWKV_WKV7_F32].pipeline;

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
                [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3];
                [encoder setBuffer:id_src4 offset:offs_src4 atIndex:4];
                [encoder setBuffer:id_src5 offset:offs_src5 atIndex:5];
                [encoder setBuffer:id_src6 offset:offs_src6 atIndex:6];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:7];

                [encoder setBytes:&B length:sizeof(B) atIndex:8];
                [encoder setBytes:&T length:sizeof(T) atIndex:9];
                [encoder setBytes:&C length:sizeof(C) atIndex:10];
                [encoder setBytes:&H length:sizeof(H) atIndex:11];

                [encoder dispatchThreadgroups:MTLSizeMake(B * H, 1, 1) threadsPerThreadgroup:MTLSizeMake(C/ H, 1, 1)];
            } break;
        case GGML_OP_MUL_MAT:
            {
                GGML_ASSERT(ne00 == ne10);

                GGML_ASSERT(ne12 % ne02 == 0);
                GGML_ASSERT(ne13 % ne03 == 0);

                const uint32_t r2 = ne12/ne02;
                const uint32_t r3 = ne13/ne03;

                // find the break-even point where the matrix-matrix kernel becomes more efficient compared
                // to the matrix-vector kernel
                const int ne11_mm_min = 4;

                // first try to use small-batch mat-mv kernels
                // these should be efficient for BS [2, ~8]
                if (src1t == GGML_TYPE_F32 && (ne00%256 == 0) &&
                    (
                     (
                      (
                       src0t == GGML_TYPE_F16  || // TODO: helper function
                       src0t == GGML_TYPE_Q4_0 ||
                       src0t == GGML_TYPE_Q4_1 ||
                       src0t == GGML_TYPE_Q5_0 ||
                       src0t == GGML_TYPE_Q5_1 ||
                       src0t == GGML_TYPE_Q8_0 ||
                       src0t == GGML_TYPE_MXFP4 ||
                       src0t == GGML_TYPE_IQ4_NL ||
                       false) && (ne11 >= 2 && ne11 <= 8)
                     ) ||
                     (
                      (
                       src0t == GGML_TYPE_Q4_K ||
                       src0t == GGML_TYPE_Q5_K ||
                       src0t == GGML_TYPE_Q6_K ||
                       false) && (ne11 >= 4 && ne11 <= 8)
                     )
                    )
                   ) {
                    // TODO: determine the optimal parameters based on grid utilization
                    //       I still don't know why we should not always use the maximum available threads:
                    //
                    //       nsg = pipeline.maxTotalThreadsPerThreadgroup / 32
                    //
                    //       my current hypothesis is that the work grid is not evenly divisible for different nsg
                    //       values and there can be some tail effects when nsg is high. need to confirm this
                    //
                    const int nsg    = 2;                 // num simdgroups per threadgroup
                    const int nxpsg  = ne11 < 3 ? 16 : 8; // num threads along row per simdgroup
                    const int nypsg  = 32/nxpsg;          // num threads along col per simdgroup (i.e. a simdgroup processes that many src0 rows at a time)
                    const int r0ptg  = nypsg*nsg;         // num src0 rows per threadgroup
                          int r1ptg  = 4;                 // num src1 rows per threadgroup

                    // note: not sure how optimal are those across all different hardware. there might be someting cleverer
                    switch (ne11) {
                        case 2:
                            r1ptg = 2; break;
                        case 3:
                        case 6:
                            r1ptg = 3; break;
                        case 4:
                        case 7:
                        case 8:
                            r1ptg = 4; break;
                        case 5:
                            r1ptg = 5; break;
                    };

                    id<MTLComputePipelineState> pipeline = nil;

                    switch (src0->type) {
                        case GGML_TYPE_F16:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_Q4_0:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_Q4_1:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_Q5_0:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_Q5_1:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_Q8_0:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_MXFP4:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_Q4_K:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_Q5_K:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_Q6_K:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        case GGML_TYPE_IQ4_NL:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_2].pipeline; break;
                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_3].pipeline; break;
                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_4].pipeline; break;
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
                        default: GGML_ABORT("not implemented");
                    }

                    ggml_metal_kargs_mul_mv_ext args = {
                        /*.ne00  =*/ ne00,
                        /*.ne01  =*/ ne01,
                        /*.ne02  =*/ ne02,
                        /*.nb00  =*/ nb00,
                        /*.nb01  =*/ nb01,
                        /*.nb02  =*/ nb02,
                        /*.nb03  =*/ nb03,
                        /*.ne10  =*/ ne10,
                        /*.ne11  =*/ ne11,
                        /*.ne12  =*/ ne12,
                        /*.nb10  =*/ nb10,
                        /*.nb11  =*/ nb11,
                        /*.nb12  =*/ nb12,
                        /*.nb13  =*/ nb13,
                        /*.ne0   =*/ ne0,
                        /*.ne1   =*/ ne1,
                        /*.r2    =*/ r2,
                        /*.r3    =*/ r3,
                        /*.nsg   =*/ nsg,
                        /*.nxpsg =*/ nxpsg,
                        /*.r1ptg =*/ r1ptg,
                    };

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBytes:&args length:sizeof(args) atIndex:0];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                    [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:3];

                    //printf("ne01 = %lld nr0ptg = %d\n", ne01, nr0ptg);
                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + r0ptg - 1)/r0ptg, (ne11 + r1ptg - 1)/r1ptg, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
                } else
                // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
                // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
                if ([device supportsFamily:MTLGPUFamilyApple7] &&
                        !ggml_is_transposed(src0) &&
                        !ggml_is_transposed(src1) &&
                        src1t == GGML_TYPE_F32 &&
                        ne00 % 32 == 0 && ne00 >= 64 &&
                        (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) {
                    //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);

                    // some Metal matrix data types require aligned pointers
                    // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
                    switch (src0->type) {
                        case GGML_TYPE_F32:  GGML_ASSERT(nb01 % 16 == 0); break;
                        case GGML_TYPE_F16:  GGML_ASSERT(nb01 % 8  == 0); break;
                        case GGML_TYPE_BF16: GGML_ASSERT(nb01 % 8  == 0); break;
                        default: break;
                    }

                    id<MTLComputePipelineState> pipeline = nil;

                    switch (src0->type) {
                        case GGML_TYPE_F32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32    ].pipeline; break;
                        case GGML_TYPE_F16:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32    ].pipeline; break;
                        case GGML_TYPE_BF16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32   ].pipeline; break;
                        case GGML_TYPE_Q4_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32   ].pipeline; break;
                        case GGML_TYPE_Q4_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32   ].pipeline; break;
                        case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32   ].pipeline; break;
                        case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32   ].pipeline; break;
                        case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32   ].pipeline; break;
                        case GGML_TYPE_MXFP4:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32   ].pipeline; break;
                        case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32   ].pipeline; break;
                        case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32   ].pipeline; break;
                        case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32   ].pipeline; break;
                        case GGML_TYPE_Q5_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32   ].pipeline; break;
                        case GGML_TYPE_Q6_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32   ].pipeline; break;
                        case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break;
                        case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break;
                        case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break;
                        case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32  ].pipeline; break;
                        case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32  ].pipeline; break;
                        case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32  ].pipeline; break;
                        case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32  ].pipeline; break;
                        case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
                        case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break;
                        default: GGML_ABORT("MUL MAT-MAT not implemented");
                    }

                    ggml_metal_kargs_mul_mm args = {
                        /*.ne00 =*/ ne00,
                        /*.ne02 =*/ ne02,
                        /*.nb01 =*/ nb01,
                        /*.nb02 =*/ nb02,
                        /*.nb03 =*/ nb03,
                        /*.ne12 =*/ ne12,
                        /*.nb10 =*/ nb10,
                        /*.nb11 =*/ nb11,
                        /*.nb12 =*/ nb12,
                        /*.nb13 =*/ nb13,
                        /*.ne0  =*/ ne0,
                        /*.ne1  =*/ ne1,
                        /*.r2   =*/ r2,
                        /*.r3   =*/ r3,
                    };

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBytes:&args    length:sizeof(args) atIndex:0];
                    [encoder setBuffer:id_src0 offset:offs_src0    atIndex:1];
                    [encoder setBuffer:id_src1 offset:offs_src1    atIndex:2];
                    [encoder setBuffer:id_dst  offset:offs_dst     atIndex:3];

                    [encoder setThreadgroupMemoryLength:8192 atIndex:0];
                    [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                } else {
                    id<MTLComputePipelineState> pipeline = nil;

                    int nsg = 0; // number of simdgroups
                    int nr0 = 0; // number of src0 rows per simdgroup
                    int nr1 = 1; // number of src1 rows per threadgroup

                    size_t smem = 0; // shared memory

                    // use custom matrix x vector kernel
                    switch (src0t) {
                        case GGML_TYPE_F32:
                            {
                                GGML_ASSERT(src1t == GGML_TYPE_F32);
                                nsg = 1;
                                nr0 = 1;
                                nr1 = 4;
                                if (ne00 == 4) {
                                    nr0 = 32;
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32_C4].pipeline;
                                } else {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline;
                                }
                            } break;
                        case GGML_TYPE_F16:
                            {
                                nsg = 1;
                                nr0 = 1;
                                if (src1t == GGML_TYPE_F32) {
                                    if (ne00 == 4) {
                                        nr0 = 32;
                                        nr1 = 4;
                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_C4].pipeline;
                                    } else if (ne11 * ne12 < 4) {
                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline;
                                    } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline;
                                        nr1 = ne11;
                                    } else {
                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline;
                                        nr1 = 4;
                                    }
                                } else {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline;
                                    nr1 = 4;
                                }
                            } break;
                        case GGML_TYPE_BF16:
                            {
                                nsg = 1;
                                nr0 = 1;
                                if (src1t == GGML_TYPE_F32) {
                                    if (ne00 == 4) {
                                        nr0 = 32;
                                        nr1 = 4;
                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_C4].pipeline;
                                    } else if (ne11 * ne12 < 4) {
                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW].pipeline;
                                    } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4].pipeline;
                                        nr1 = ne11;
                                    } else {
                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32].pipeline;
                                        nr1 = 4;
                                    }
                                } else {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16].pipeline;
                                    nr1 = 4;
                                }
                            } break;
                        case GGML_TYPE_Q4_0:
                            {
                                nsg = N_SG_Q4_0;
                                nr0 = N_R0_Q4_0;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q4_1:
                            {
                                nsg = N_SG_Q4_1;
                                nr0 = N_R0_Q4_1;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q5_0:
                            {
                                nsg = N_SG_Q5_0;
                                nr0 = N_R0_Q5_0;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q5_1:
                            {
                                nsg = N_SG_Q5_1;
                                nr0 = N_R0_Q5_1;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q8_0:
                            {
                                nsg = N_SG_Q8_0;
                                nr0 = N_R0_Q8_0;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline;
                            } break;
                        case GGML_TYPE_MXFP4:
                            {
                                nsg = N_SG_MXFP4;
                                nr0 = N_R0_MXFP4;
                                smem = 32*sizeof(float);
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q2_K:
                            {
                                nsg = N_SG_Q2_K;
                                nr0 = N_R0_Q2_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q3_K:
                            {
                                nsg = N_SG_Q3_K;
                                nr0 = N_R0_Q3_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q4_K:
                            {
                                nsg = N_SG_Q4_K;
                                nr0 = N_R0_Q4_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q5_K:
                            {
                                nsg = N_SG_Q5_K;
                                nr0 = N_R0_Q5_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q6_K:
                            {
                                nsg = N_SG_Q6_K;
                                nr0 = N_R0_Q6_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ2_XXS:
                            {
                                nsg = N_SG_IQ2_XXS;
                                nr0 = N_R0_IQ2_XXS;
                                smem = 256*8+128;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ2_XS:
                            {
                                nsg = N_SG_IQ2_XS;
                                nr0 = N_R0_IQ2_XS;
                                smem = 512*8+128;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ3_XXS:
                            {
                                nsg = N_SG_IQ3_XXS;
                                nr0 = N_R0_IQ3_XXS;
                                smem = 256*4+128;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ3_S:
                            {
                                nsg = N_SG_IQ3_S;
                                nr0 = N_R0_IQ3_S;
                                smem = 512*4;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ2_S:
                            {
                                nsg = N_SG_IQ2_S;
                                nr0 = N_R0_IQ2_S;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ1_S:
                            {
                                nsg = N_SG_IQ1_S;
                                nr0 = N_R0_IQ1_S;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ1_M:
                            {
                                nsg = N_SG_IQ1_M;
                                nr0 = N_R0_IQ1_M;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ4_NL:
                            {
                                nsg = N_SG_IQ4_NL;
                                nr0 = N_R0_IQ4_NL;
                                smem = 32*sizeof(float);
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ4_XS:
                            {
                                nsg = N_SG_IQ4_XS;
                                nr0 = N_R0_IQ4_XS;
                                smem = 32*sizeof(float);
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline;
                            } break;
                        default:
                            {
                                GGML_LOG_ERROR("Asserting on type %d\n", (int)src0t);
                                GGML_ABORT("not implemented");
                            }
                    };

                    ggml_metal_kargs_mul_mv args = {
                        /*.ne00 =*/ ne00,
                        /*.ne01 =*/ ne01,
                        /*.ne02 =*/ ne02,
                        /*.nb00 =*/ nb00,
                        /*.nb01 =*/ nb01,
                        /*.nb02 =*/ nb02,
                        /*.nb03 =*/ nb03,
                        /*.ne10 =*/ ne10,
                        /*.ne11 =*/ ne11,
                        /*.ne12 =*/ ne12,
                        /*.nb10 =*/ nb10,
                        /*.nb11 =*/ nb11,
                        /*.nb12 =*/ nb12,
                        /*.nb13 =*/ nb13,
                        /*.ne0  =*/ ne0,
                        /*.ne1  =*/ ne1,
                        /*.r2   =*/ r2,
                        /*.r3   =*/ r3,
                    };

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBytes:&args length:sizeof(args) atIndex:0];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                    [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:3];

                    if (smem > 0) {
                        [encoder setThreadgroupMemoryLength:smem atIndex:0];
                    }
                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nr0*nsg - 1)/(nr0*nsg), (ne11 + nr1 - 1)/nr1, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
                }
            } break;
        case GGML_OP_MUL_MAT_ID:
            {
                // src2 = ids
                GGML_ASSERT(src2t == GGML_TYPE_I32);

                GGML_ASSERT(!ggml_is_transposed(src0));
                GGML_ASSERT(!ggml_is_transposed(src1));

                GGML_ASSERT(src1t == GGML_TYPE_F32);

                GGML_ASSERT(ne03 == 1);
                GGML_ASSERT(ne13 == 1);

                const uint32_t r2 = 1;
                const uint32_t r3 = 1;

                // find the break-even point where the matrix-matrix kernel becomes more efficient compared
                // to the matrix-vector kernel
                // ne20 = n_used_experts
                // ne21 = n_rows (batch size)
                const int ne21_mm_id_min = 32;

                // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
                // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
                if ([device supportsFamily:MTLGPUFamilyApple7] &&
                        ne00 % 32 == 0 && ne00 >= 64 &&
                        (ne21 >= ne21_mm_id_min)) {
                    GGML_ASSERT(ne00 % 4 == 0);

                    // some Metal matrix data types require aligned pointers
                    // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
                    switch (src0->type) {
                        case GGML_TYPE_F32:  GGML_ASSERT(nb01 % 16 == 0); break;
                        case GGML_TYPE_F16:  GGML_ASSERT(nb01 % 8  == 0); break;
                        case GGML_TYPE_BF16: GGML_ASSERT(nb01 % 8  == 0); break;
                        default: break;
                    }

                    const int64_t neh10 = ne10; // n_embd
                    const int64_t neh11 = ne21; // n_tokens
                    const int64_t neh12 = ne02; // n_expert

                    const uint64_t nbh10 = ggml_type_size(GGML_TYPE_F16);
                    const uint64_t nbh11 = nbh10*neh10;
                    const uint64_t nbh12 = nbh11*neh11;
                    const uint64_t nbh13 = nbh12*neh12;

                    const size_t s_src1 = ggml_type_size(GGML_TYPE_F16)*neh10*neh11*neh12;
                    id<MTLBuffer> h_src1 = ggml_metal_mem_pool_alloc(mem_pool, s_src1);
                    if (!h_src1) {
                        GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, s_src1);
                        return 0;
                    }

                    const int64_t neh0 = ne0;
                    const int64_t neh1 = ne21;
                    const int64_t neh2 = ne02;

                    const uint64_t nbh0 = ggml_type_size(GGML_TYPE_F32);
                    const uint64_t nbh1 = nbh0*neh0;
                    const uint64_t nbh2 = nbh1*neh1;
                  //const uint64_t nbh3 = nbh2*neh2;

                    const size_t s_dst = ggml_type_size(GGML_TYPE_F32)*neh0*neh1*neh2;
                    id<MTLBuffer> h_dst = ggml_metal_mem_pool_alloc(mem_pool, s_dst);
                    if (!h_dst) {
                        GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, s_dst);
                        return 0;
                    }

                    // tokens per expert
                    const size_t s_tpe = ggml_type_size(GGML_TYPE_I32)*ne02;
                    id<MTLBuffer> h_tpe = ggml_metal_mem_pool_alloc(mem_pool, s_tpe);
                    if (!h_tpe) {
                        GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, s_tpe);
                        return 0;
                    }

                    // id map
                    // [n_expert_used, n_tokens]
                    const size_t s_ids = ggml_type_size(GGML_TYPE_I32)*ne20*ne21;
                    id<MTLBuffer> h_ids = ggml_metal_mem_pool_alloc(mem_pool, s_ids);
                    if (!h_ids) {
                        GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, s_ids);
                        return 0;
                    }

                    {
                        const int nth = MIN(1024, ne10/4);

                        ggml_metal_kargs_mul_mm_id_map0 args = {
                            ne10,
                            ne11,  // n_expert_used (bcast)
                            nb11,
                            nb12,
                            neh11, // n_tokens
                            nbh11,
                            ne20,  // n_expert_used
                            nb21,
                        };

                        id<MTLComputePipelineState> pipeline = nil;

                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16].pipeline;

                        [encoder setComputePipelineState:pipeline];
                        [encoder setBytes:&args    length:sizeof(args) atIndex:0];
                        [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
                        [encoder setBuffer:id_src2 offset:offs_src2    atIndex:2];
                        [encoder setBuffer: h_src1 offset:0            atIndex:3];
                        [encoder setBuffer: h_tpe  offset:0            atIndex:4];
                        [encoder setBuffer: h_ids  offset:0            atIndex:5];

                        [encoder dispatchThreadgroups:MTLSizeMake(ne02, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                    }

                    {
                        id<MTLComputePipelineState> pipeline = nil;

                        switch (src0->type) {
                            case GGML_TYPE_F32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F16    ].pipeline; break;
                            case GGML_TYPE_F16:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F16    ].pipeline; break;
                            case GGML_TYPE_BF16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F16   ].pipeline; break;
                            case GGML_TYPE_Q4_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F16   ].pipeline; break;
                            case GGML_TYPE_Q4_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F16   ].pipeline; break;
                            case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16   ].pipeline; break;
                            case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16   ].pipeline; break;
                            case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16   ].pipeline; break;
                            case GGML_TYPE_MXFP4:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16  ].pipeline; break;
                            case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16   ].pipeline; break;
                            case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16   ].pipeline; break;
                            case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16   ].pipeline; break;
                            case GGML_TYPE_Q5_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F16   ].pipeline; break;
                            case GGML_TYPE_Q6_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F16   ].pipeline; break;
                            case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F16].pipeline; break;
                            case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F16 ].pipeline; break;
                            case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F16].pipeline; break;
                            case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F16  ].pipeline; break;
                            case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F16  ].pipeline; break;
                            case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F16  ].pipeline; break;
                            case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F16  ].pipeline; break;
                            case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F16 ].pipeline; break;
                            case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F16 ].pipeline; break;
                            default: GGML_ABORT("MUL_MAT_ID not implemented");
                        }

                        ggml_metal_kargs_mul_mm_id args = {
                            /*.ne00  =*/ ne00,
                            /*.ne02  =*/ ne02,
                            /*.nb01  =*/ nb01,
                            /*.nb02  =*/ nb02,
                            /*.nb03  =*/ nb03,
                            /*.neh12 =*/ neh12,
                            /*.nbh10 =*/ nbh10,
                            /*.nbh11 =*/ nbh11,
                            /*.nbh12 =*/ nbh12,
                            /*.nbh13 =*/ nbh13,
                            /*.neh0  =*/ neh0,
                            /*.neh1  =*/ neh1,
                            /*.r2    =*/ r2,
                            /*.r3    =*/ r3,
                        };

                        [encoder setComputePipelineState:pipeline];
                        [encoder setBytes:&args    length:sizeof(args) atIndex:0];
                        [encoder setBuffer:id_src0 offset:offs_src0    atIndex:1];
                        [encoder setBuffer: h_src1 offset:0            atIndex:2];
                        [encoder setBuffer: h_tpe  offset:0            atIndex:3];
                        [encoder setBuffer: h_dst  offset:0            atIndex:4];

                        [encoder setThreadgroupMemoryLength:8192 atIndex:0];
                        [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, ne02) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                    }

                    {
                        GGML_ASSERT(ne0 % 4 == 0);

                        const int nth = MIN(1024, ne0/4);

                        ggml_metal_kargs_mul_mm_id_map1 args = {
                            ne20, // n_expert_used
                            neh0,
                            neh1,
                            nbh1,
                            nbh2,
                            ne0,
                            nb1,
                            nb2,
                        };

                        id<MTLComputePipelineState> pipeline = nil;

                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP1_F32].pipeline;

                        [encoder setComputePipelineState:pipeline];
                        [encoder setBytes:&args   length:sizeof(args) atIndex:0];
                        [encoder setBuffer: h_dst offset:0            atIndex:1];
                        [encoder setBuffer: h_ids offset:0            atIndex:2];
                        [encoder setBuffer:id_dst offset:offs_dst     atIndex:3];

                        [encoder dispatchThreadgroups:MTLSizeMake(ne20, ne21, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                    }
                } else {
                    id<MTLComputePipelineState> pipeline = nil;

                    int nsg = 0; // number of simdgroups
                    int nr0 = 0; // number of src0 rows per simdgroup
                    int nr1 = 1; // number of src1 rows per threadgroup

                    size_t smem = 0; // shared memory

                    // use custom matrix x vector kernel
                    switch (src0t) {
                        case GGML_TYPE_F32:
                            {
                                GGML_ASSERT(src1t == GGML_TYPE_F32);
                                nsg = 1;
                                nr0 = 1;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline;
                            } break;
                        case GGML_TYPE_F16:
                            {
                                GGML_ASSERT(src1t == GGML_TYPE_F32);
                                nsg = 1;
                                nr0 = 1;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline;
                            } break;
                        case GGML_TYPE_BF16:
                            {
                                GGML_ASSERT(src1t == GGML_TYPE_F32);
                                nsg = 1;
                                nr0 = 1;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q4_0:
                            {
                                nsg = N_SG_Q4_0;
                                nr0 = N_R0_Q4_0;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q4_1:
                            {
                                nsg = N_SG_Q4_1;
                                nr0 = N_R0_Q4_1;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q5_0:
                            {
                                nsg = N_SG_Q5_0;
                                nr0 = N_R0_Q5_0;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q5_1:
                            {
                                nsg = N_SG_Q5_1;
                                nr0 = N_R0_Q5_1;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q8_0:
                            {
                                nsg = N_SG_Q8_0;
                                nr0 = N_R0_Q8_0;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline;
                            } break;
                        case GGML_TYPE_MXFP4:
                            {
                                nsg = N_SG_MXFP4;
                                nr0 = N_R0_MXFP4;
                                smem = 32*sizeof(float);
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q2_K:
                            {
                                nsg = N_SG_Q2_K;
                                nr0 = N_R0_Q2_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q3_K:
                            {
                                nsg = N_SG_Q3_K;
                                nr0 = N_R0_Q3_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q4_K:
                            {
                                nsg = N_SG_Q4_K;
                                nr0 = N_R0_Q4_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q5_K:
                            {
                                nsg = N_SG_Q5_K;
                                nr0 = N_R0_Q5_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_Q6_K:
                            {
                                nsg = N_SG_Q6_K;
                                nr0 = N_R0_Q6_K;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ2_XXS:
                            {
                                nsg = N_SG_IQ2_XXS;
                                nr0 = N_R0_IQ2_XXS;
                                smem = 256*8+128;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ2_XS:
                            {
                                nsg = N_SG_IQ2_XS;
                                nr0 = N_R0_IQ2_XS;
                                smem = 512*8+128;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ3_XXS:
                            {
                                nsg = N_SG_IQ3_XXS;
                                nr0 = N_R0_IQ3_XXS;
                                smem = 256*4+128;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ3_S:
                            {
                                nsg = N_SG_IQ3_S;
                                nr0 = N_R0_IQ3_S;
                                smem = 512*4;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ2_S:
                            {
                                nsg = N_SG_IQ2_S;
                                nr0 = N_R0_IQ2_S;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ1_S:
                            {
                                nsg = N_SG_IQ1_S;
                                nr0 = N_R0_IQ1_S;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ1_M:
                            {
                                nsg = N_SG_IQ1_M;
                                nr0 = N_R0_IQ1_M;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ4_NL:
                            {
                                nsg = N_SG_IQ4_NL;
                                nr0 = N_R0_IQ4_NL;
                                smem = 32*sizeof(float);
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
                            } break;
                        case GGML_TYPE_IQ4_XS:
                            {
                                nsg = N_SG_IQ4_XS;
                                nr0 = N_R0_IQ4_XS;
                                smem = 32*sizeof(float);
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
                            } break;
                        default:
                            {
                                GGML_LOG_ERROR("Asserting on type %d\n", (int)src2t);
                                GGML_ABORT("not implemented");
                            }
                    };

                    if (ggml_is_quantized(src0t)) {
                        GGML_ASSERT(ne00 >= nsg*nr0);
                    }

                    ggml_metal_kargs_mul_mv_id args = {
                        /*.nei0 =*/ ne20,
                        /*.nei1 =*/ ne21,
                        /*.nbi1 =*/ nb21,
                        /*.ne00 =*/ ne00,
                        /*.ne01 =*/ ne01,
                        /*.ne02 =*/ ne02,
                        /*.nb00 =*/ nb00,
                        /*.nb01 =*/ nb01,
                        /*.nb02 =*/ nb02,
                        /*.ne10 =*/ ne10,
                        /*.ne11 =*/ ne11,
                        /*.ne12 =*/ ne12,
                        /*.ne13 =*/ ne13,
                        /*.nb10 =*/ nb10,
                        /*.nb11 =*/ nb11,
                        /*.nb12 =*/ nb12,
                        /*.ne0  =*/ ne0,
                        /*.ne1  =*/ ne1,
                        /*.nb1  =*/ nb1,
                    };

                    [encoder setComputePipelineState:pipeline];
                    [encoder setBytes:&args length:sizeof(args) atIndex:0];
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                    [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:3];
                    [encoder setBuffer:id_src2 offset:offs_src2 atIndex:4];

                    const int64_t _ne1 = 1;
                    const int64_t ne123 = ne20*ne21;

                    if (smem > 0) {
                        [encoder setThreadgroupMemoryLength:smem atIndex:0];
                    }
                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nr0*nsg - 1)/(nr0*nsg), (_ne1 + nr1 - 1)/nr1, ne123) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
                }
            } break;
        case GGML_OP_GET_ROWS:
            {
                id<MTLComputePipelineState> pipeline = nil;

                switch (src0->type) {
                    case GGML_TYPE_F32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32    ].pipeline; break;
                    case GGML_TYPE_F16:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16    ].pipeline; break;
                    case GGML_TYPE_BF16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16   ].pipeline; break;
                    case GGML_TYPE_Q4_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0   ].pipeline; break;
                    case GGML_TYPE_Q4_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1   ].pipeline; break;
                    case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0   ].pipeline; break;
                    case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1   ].pipeline; break;
                    case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0   ].pipeline; break;
                    case GGML_TYPE_MXFP4:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4  ].pipeline; break;
                    case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K   ].pipeline; break;
                    case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K   ].pipeline; break;
                    case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K   ].pipeline; break;
                    case GGML_TYPE_Q5_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K   ].pipeline; break;
                    case GGML_TYPE_Q6_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K   ].pipeline; break;
                    case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break;
                    case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break;
                    case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break;
                    case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S  ].pipeline; break;
                    case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S  ].pipeline; break;
                    case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S  ].pipeline; break;
                    case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M  ].pipeline; break;
                    case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
                    case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break;
                    case GGML_TYPE_I32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32    ].pipeline; break;
                    default: GGML_ABORT("not implemented");
                }

                ggml_metal_kargs_get_rows args = {
                    /*.ne00 =*/ ne00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.ne10 =*/ ne10,
                    /*.nb10 =*/ nb10,
                    /*.nb11 =*/ nb11,
                    /*.nb1 =*/ nb1,
                    /*.nb2 =*/ nb2,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args    length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0    atIndex:1];
                [encoder setBuffer:id_src1 offset:offs_src1    atIndex:2];
                [encoder setBuffer:id_dst  offset:offs_dst     atIndex:3];

                [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
            } break;
        case GGML_OP_SET_ROWS:
            {
                id<MTLComputePipelineState> pipeline = nil;

                switch (dst->type) {
                    case GGML_TYPE_F32:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_F32   ].pipeline; break;
                    case GGML_TYPE_F16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_F16   ].pipeline; break;
                    case GGML_TYPE_BF16:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_BF16  ].pipeline; break;
                    case GGML_TYPE_Q8_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_Q8_0  ].pipeline; break;
                    case GGML_TYPE_Q4_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_Q4_0  ].pipeline; break;
                    case GGML_TYPE_Q4_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_Q4_1  ].pipeline; break;
                    case GGML_TYPE_Q5_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_Q5_0  ].pipeline; break;
                    case GGML_TYPE_Q5_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_Q5_1  ].pipeline; break;
                    case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_ROWS_IQ4_NL].pipeline; break;
                    default: GGML_ABORT("not implemented");
                }

                const int32_t nk0 = ne0/ggml_blck_size(dst->type);

                int nth = 32; // SIMD width

                while (nth < nk0 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
                    nth *= 2;
                }

                int nrptg = 1;
                if (nth > nk0) {
                    nrptg = (nth + nk0 - 1)/nk0;
                    nth   = nk0;

                    if (nrptg*nth > (int) pipeline.maxTotalThreadsPerThreadgroup) {
                        nrptg--;
                    }
                }

                nth = MIN(nth, nk0);

                ggml_metal_kargs_set_rows args = {
                    /*.nk0  =*/ nk0,
                    /*.ne01 =*/ ne01,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne11 =*/ ne11,
                    /*.ne12 =*/ ne12,
                    /*.nb10 =*/ nb10,
                    /*.nb11 =*/ nb11,
                    /*.nb12 =*/ nb12,
                    /*.nb1  =*/ nb1,
                    /*.nb2  =*/ nb2,
                    /*.nb3  =*/ nb3,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args    length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0    atIndex:1];
                [encoder setBuffer:id_src1 offset:offs_src1    atIndex:2];
                [encoder setBuffer:id_dst  offset:offs_dst     atIndex:3];

                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nrptg - 1)/nrptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, nrptg, 1)];
            } break;
        case GGML_OP_RMS_NORM:
            {
                GGML_ASSERT(ne00 % 4 == 0);
                GGML_ASSERT(ggml_is_contiguous_rows(src0));

                float eps;
                memcpy(&eps, dst->op_params, sizeof(float));

                ggml_metal_kargs_rms_norm args = {
                    /*.ne00   =*/ ne00,
                    /*.ne00_4 =*/ ne00/4,
                    /*.nb1    =*/ nb1,
                    /*.nb2    =*/ nb2,
                    /*.nb3    =*/ nb3,
                    /*.eps    =*/ eps,
                    /*.nef1   =*/ { ne01 },
                    /*.nef2   =*/ { ne02 },
                    /*.nef3   =*/ { ne03 },
                    /*.nbf1   =*/ { nb01 },
                    /*.nbf2   =*/ { nb02 },
                    /*.nbf3   =*/ { nb03 },
                };

                size_t offs_fuse[2] = { 0, 0 };
                id<MTLBuffer> id_fuse[2] = { id_src0, id_src0 };

                // d[0] = rms_norm(a)
                // d[1] = mul(d[0], b)
                // d[2] = add(d[1], c)
                if (ctx_dev->use_fusion) {
                    ops[0] = GGML_OP_RMS_NORM;
                    ops[1] = GGML_OP_MUL;
                    ops[2] = GGML_OP_ADD;

                    for (n_fuse = 0; n_fuse <= 1 && idx + n_fuse + 1 < idx_end; ++n_fuse) {
                        if (!ggml_can_fuse(gf, idx + n_fuse, ops + n_fuse, 2)) {
                            break;
                        }

                        if (nodes[n_fuse] != nodes[n_fuse + 1]->src[0]) {
                            break;
                        }

                        if (nodes[n_fuse + 1]->src[1]->ne[0] != node->ne[0]) {
                            break;
                        }

                        if (!ggml_is_contiguous_rows(nodes[n_fuse + 1]->src[1])) {
                            break;
                        }

                        if (nodes[n_fuse + 1]->type != GGML_TYPE_F32) {
                            break;
                        }

                        ctx_dev->fuse_cnt[nodes[n_fuse + 1]->op]++;

                        id_fuse[n_fuse] = ggml_metal_get_buffer(nodes[n_fuse + 1]->src[1], &offs_fuse[n_fuse]);

                        args.nef1[n_fuse + 1] = nodes[n_fuse + 1]->src[1]->ne[1];
                        args.nef2[n_fuse + 1] = nodes[n_fuse + 1]->src[1]->ne[2];
                        args.nef3[n_fuse + 1] = nodes[n_fuse + 1]->src[1]->ne[3];

                        args.nbf1[n_fuse + 1] = nodes[n_fuse + 1]->src[1]->nb[1];
                        args.nbf2[n_fuse + 1] = nodes[n_fuse + 1]->src[1]->nb[2];
                        args.nbf3[n_fuse + 1] = nodes[n_fuse + 1]->src[1]->nb[3];
                    }

                    ++n_fuse;

                    if (ctx_dev->debug_fusion > 1 && n_fuse > 1) {
                        if (n_fuse == 2) {
                            GGML_LOG_DEBUG("%s: fuse: RMS_NORM + MUL\n", __func__);
                        }
                        if (n_fuse == 3) {
                            GGML_LOG_DEBUG("%s: fuse: RMS_NORM + MUL + ADD\n", __func__);
                        }
                    }
                }

                if (n_fuse > 1) {
                    id_dst = ggml_metal_get_buffer(nodes[n_fuse - 1], &offs_dst);
                }

                id<MTLComputePipelineState> pipeline;

                switch (n_fuse) {
                    case 1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM        ].pipeline; break;
                    case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM_MUL    ].pipeline; break;
                    case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM_MUL_ADD].pipeline; break;
                    default: GGML_ABORT("unsupported n_fuse = %d\n", n_fuse);
                }

                int nth = 32; // SIMD width

                while (nth < ne00/4 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
                    nth *= 2;
                }

                nth = MIN(nth, (int) pipeline.maxTotalThreadsPerThreadgroup);
                nth = MIN(nth, ne00/4);

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args)       atIndex:0];
                [encoder setBuffer:id_src0    offset:offs_src0    atIndex:1];
                [encoder setBuffer:id_fuse[0] offset:offs_fuse[0] atIndex:2];
                [encoder setBuffer:id_fuse[1] offset:offs_fuse[1] atIndex:3];
                [encoder setBuffer:id_dst     offset:offs_dst     atIndex:4];

                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];

                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_L2_NORM:
            {
                GGML_ASSERT(ne00 % 4 == 0);
                GGML_ASSERT(ggml_is_contiguous_1(src0));

                float eps;
                memcpy(&eps, dst->op_params, sizeof(float));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_L2_NORM].pipeline;

                int nth = 32; // SIMD width

                while (nth < ne00/4 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
                    nth *= 2;
                }

                nth = MIN(nth, (int) pipeline.maxTotalThreadsPerThreadgroup);
                nth = MIN(nth, ne00/4);

                ggml_metal_kargs_l2_norm args = {
                    /*.ne00   =*/ ne00,
                    /*.ne00_4 =*/ ne00/4,
                    /*.nb01   =*/ nb01,
                    /*.eps    =*/ eps,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];

                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];

                const int64_t nrows = ggml_nrows(src0);

                [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_GROUP_NORM:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));

                float eps;
                memcpy(&eps, dst->op_params + 1, sizeof(float));

                const int32_t n_groups = ((const int32_t *) dst->op_params)[0];

                int nth = 32; // SIMD width

                //while (nth < ne00/4 && nth < 1024) {
                //    nth *= 2;
                //}

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline;

                ggml_metal_kargs_group_norm args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.n_groups =*/ n_groups,
                    /*.eps =*/ eps,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0  offset:offs_src0        atIndex:0];
                [encoder setBuffer:id_dst   offset:offs_dst         atIndex:1];
                [encoder setBytes:&args     length:sizeof(args)     atIndex:2];
                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];

                [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_NORM:
            {
                GGML_ASSERT(ne00 % 4 == 0);
                GGML_ASSERT(ggml_is_contiguous_1(src0));

                float eps;
                memcpy(&eps, dst->op_params, sizeof(float));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline;

                int nth = 32; // SIMD width

                while (nth < ne00/4 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
                    nth *= 2;
                }

                nth = MIN(nth, (int) pipeline.maxTotalThreadsPerThreadgroup);
                nth = MIN(nth, ne00/4);

                ggml_metal_kargs_norm args = {
                    /*.ne00   =*/ ne00,
                    /*.ne00_4 =*/ ne00/4,
                    /*.nb01   =*/ nb01,
                    /*.eps    =*/ eps,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];

                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];

                const int64_t nrows = ggml_nrows(src0);

                [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_ROPE:
            {

                // make sure we have one or more position id(ne10) per token(ne02)
                GGML_ASSERT(ne10 % ne02 == 0);
                GGML_ASSERT(ne10 >= ne02);

                const int nth = MIN(1024, ne00);

                const int n_past     = ((const int32_t *) dst->op_params)[0];
                const int n_dims     = ((const int32_t *) dst->op_params)[1];
                const int mode       = ((const int32_t *) dst->op_params)[2];
                // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
                const int n_ctx_orig = ((const int32_t *) dst->op_params)[4];

                float freq_base;
                float freq_scale;
                float ext_factor;
                float attn_factor;
                float beta_fast;
                float beta_slow;

                memcpy(&freq_base,   (const int32_t *) dst->op_params +  5, sizeof(float));
                memcpy(&freq_scale,  (const int32_t *) dst->op_params +  6, sizeof(float));
                memcpy(&ext_factor,  (const int32_t *) dst->op_params +  7, sizeof(float));
                memcpy(&attn_factor, (const int32_t *) dst->op_params +  8, sizeof(float));
                memcpy(&beta_fast,   (const int32_t *) dst->op_params +  9, sizeof(float));
                memcpy(&beta_slow,   (const int32_t *) dst->op_params + 10, sizeof(float));

                const bool is_neox   = mode & GGML_ROPE_TYPE_NEOX;
                const bool is_mrope  = mode & GGML_ROPE_TYPE_MROPE;
                const bool is_vision = mode == GGML_ROPE_TYPE_VISION;

                // mrope
                const int sect_0 = ((const int32_t *) dst->op_params)[11];
                const int sect_1 = ((const int32_t *) dst->op_params)[12];
                const int sect_2 = ((const int32_t *) dst->op_params)[13];
                const int sect_3 = ((const int32_t *) dst->op_params)[14];

                id<MTLComputePipelineState> pipeline = nil;

                if (is_neox) {
                    switch (src0->type) {
                        case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break;
                        case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break;
                        default: GGML_ABORT("fatal error");
                    };
                } else if (is_mrope && !is_vision) {
                    GGML_ASSERT(ne10*4 >= ne02); // need at least 4 pos per token
                    switch (src0->type) {
                        case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F32].pipeline; break;
                        case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_MULTI_F16].pipeline; break;
                        default: GGML_ABORT("fatal error");
                    };
                } else if (is_vision) {
                    GGML_ASSERT(ne10*4 >= ne02); // need at least 4 pos per token
                    switch (src0->type) {
                        case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_VISION_F32].pipeline; break;
                        case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_VISION_F16].pipeline; break;
                        default: GGML_ABORT("fatal error");
                    };
                } else {
                    switch (src0->type) {
                        case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break;
                        case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break;
                        default: GGML_ABORT("fatal error");
                    };
                }

                ggml_metal_kargs_rope args = {
                    /*.ne00        =*/ ne00,
                    /*.ne01        =*/ ne01,
                    /*.ne02        =*/ ne02,
                    /*.ne03        =*/ ne03,
                    /*.nb00        =*/ nb00,
                    /*.nb01        =*/ nb01,
                    /*.nb02        =*/ nb02,
                    /*.nb03        =*/ nb03,
                    /*.ne0         =*/ ne0,
                    /*.ne1         =*/ ne1,
                    /*.ne2         =*/ ne2,
                    /*.ne3         =*/ ne3,
                    /*.nb0         =*/ nb0,
                    /*.nb1         =*/ nb1,
                    /*.nb2         =*/ nb2,
                    /*.nb3         =*/ nb3,
                    /*.n_past      =*/ n_past,
                    /*.n_dims      =*/ n_dims,
                    /*.n_ctx_orig  =*/ n_ctx_orig,
                    /*.freq_base   =*/ freq_base,
                    /*.freq_scale  =*/ freq_scale,
                    /*.ext_factor  =*/ ext_factor,
                    /*.attn_factor =*/ attn_factor,
                    /*.beta_fast   =*/ beta_fast,
                    /*.beta_slow   =*/ beta_slow,
                    /* sect_0      =*/ sect_0,
                    /* sect_1      =*/ sect_1,
                    /* sect_2      =*/ sect_2,
                    /* sect_3      =*/ sect_3,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args)     atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0     atIndex:1];
                [encoder setBuffer:id_src1 offset:offs_src1     atIndex:2];
                if (id_src2 != nil) {
                    [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3];
                } else {
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3];
                }
                [encoder setBuffer:id_dst  offset:offs_dst      atIndex:4];

                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_IM2COL:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));
                GGML_ASSERT(ggml_is_contiguous(src1));
                GGML_ASSERT(src0->type == GGML_TYPE_F16);
                GGML_ASSERT(src1->type == GGML_TYPE_F32);
                GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);

                const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
                const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
                const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
                const int32_t p1 = ((const int32_t *)(dst->op_params))[3];
                const int32_t d0 = ((const int32_t *)(dst->op_params))[4];
                const int32_t d1 = ((const int32_t *)(dst->op_params))[5];

                const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1;

                const int32_t N  = src1->ne[is_2D ? 3 : 2];
                const int32_t IC = src1->ne[is_2D ? 2 : 1];
                const int32_t IH = is_2D ? src1->ne[1] : 1;
                const int32_t IW =         src1->ne[0];

                const int32_t KH = is_2D ? src0->ne[1] : 1;
                const int32_t KW =         src0->ne[0];

                const int32_t OH = is_2D ? dst->ne[2] : 1;
                const int32_t OW =         dst->ne[1];

                const int32_t CHW = IC * KH * KW;

                const uint64_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4;
                const uint64_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4;

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline;

                const bool is_gt_mttpt = ((size_t)(N * KH * KW)) > pipeline.maxTotalThreadsPerThreadgroup;

                switch (dst->type) {
                    case GGML_TYPE_F32: {
                        pipeline = (is_gt_mttpt ?
                                    ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32].pipeline
                                    :
                                    ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline);
                    } break;
                    case GGML_TYPE_F16: {
                        pipeline = (is_gt_mttpt ?
                                    ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16].pipeline
                                    :
                                    ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline);
                    } break;
                    default: GGML_ABORT("fatal error");
                };

                ggml_metal_kargs_im2col args = {
                    /*.ofs0 =*/ ofs0,
                    /*.ofs1 =*/ ofs1,
                    /*.IW   =*/ IW,
                    /*.IH   =*/ IH,
                    /*.CHW  =*/ CHW,
                    /*.s0   =*/ s0,
                    /*.s1   =*/ s1,
                    /*.p0   =*/ p0,
                    /*.p1   =*/ p1,
                    /*.d0   =*/ d0,
                    /*.d1   =*/ d1,
                    /*.N    =*/ N,
                    /*.KH   =*/ KH,
                    /*.KW   =*/ KW,
                    /*.KHW  =*/ KH * KW,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src1 offset:offs_src1       atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst        atIndex:1];
                [encoder setBytes:&args length:sizeof(args)       atIndex:2];

                if (is_gt_mttpt) {
                    const uint64_t n_threads = MIN(pipeline.maxTotalThreadsPerThreadgroup, (uint64_t)N);

                    const int64_t  quotient  = N / n_threads + (N % n_threads > 0 ? 1 : 0);

                    [encoder dispatchThreadgroups:MTLSizeMake(quotient * CHW, OH, OW) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)];
                } else {
                    [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)];
                }
            } break;
        case GGML_OP_CONV_TRANSPOSE_1D:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));
                GGML_ASSERT(ggml_is_contiguous(src1));
                GGML_ASSERT(src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32);
                GGML_ASSERT(src1->type == GGML_TYPE_F32);
                GGML_ASSERT( dst->type == GGML_TYPE_F32);

                const int32_t s0 = ((const int32_t *)(dst->op_params))[0];

                const int32_t IC = src1->ne[1];
                const int32_t IL = src1->ne[0];

                const int32_t K  = src0->ne[0];

                const int32_t OL = dst->ne[0];
                const int32_t OC = dst->ne[1];

                id<MTLComputePipelineState> pipeline;

                switch (src0->type) {
                    case GGML_TYPE_F32: {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F32_F32].pipeline;
                    } break;
                    case GGML_TYPE_F16: {
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F16_F32].pipeline;
                    } break;
                    default: GGML_ABORT("fatal error");
                };

                ggml_metal_kargs_conv_transpose_1d args = {
                    /*.IC =*/ IC,
                    /*.IL =*/ IL,
                    /*.K  =*/ K,
                    /*.s0 =*/ s0,
                    /*.nb0 =*/ nb0,
                    /*.nb1 =*/ nb1,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0         atIndex:0];
                [encoder setBuffer:id_src1 offset:offs_src1         atIndex:1];
                [encoder setBuffer:id_dst  offset:offs_dst          atIndex:2];
                [encoder setBytes:&args    length:sizeof(args)       atIndex:3];

                [encoder dispatchThreadgroups:MTLSizeMake(OL, OC, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_UPSCALE:
            {
                GGML_ASSERT(src0->type == GGML_TYPE_F32);

                const float sf0 = (float)ne0/src0->ne[0];
                const float sf1 = (float)ne1/src0->ne[1];
                const float sf2 = (float)ne2/src0->ne[2];
                const float sf3 = (float)ne3/src0->ne[3];

                const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline;

                ggml_metal_kargs_upscale args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne0 =*/ ne0,
                    /*.ne1 =*/ ne1,
                    /*.ne2 =*/ ne2,
                    /*.ne3 =*/ ne3,
                    /*.nb0 =*/ nb0,
                    /*.nb1 =*/ nb1,
                    /*.nb2 =*/ nb2,
                    /*.nb3 =*/ nb3,
                    /*.sf0 =*/ sf0,
                    /*.sf1 =*/ sf1,
                    /*.sf2 =*/ sf2,
                    /*.sf3 =*/ sf3
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                [encoder setBytes:&args length:sizeof(args) atIndex:2];

                const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);

                [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_PAD:
            {
                GGML_ASSERT(src0->type == GGML_TYPE_F32);

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline;

                ggml_metal_kargs_pad args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne0 =*/ ne0,
                    /*.ne1 =*/ ne1,
                    /*.ne2 =*/ ne2,
                    /*.ne3 =*/ ne3,
                    /*.nb0 =*/ nb0,
                    /*.nb1 =*/ nb1,
                    /*.nb2 =*/ nb2,
                    /*.nb3 =*/ nb3
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                [encoder setBytes:&args length:sizeof(args) atIndex:2];

                const int nth = MIN(1024, ne0);

                [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_PAD_REFLECT_1D:
            {
                GGML_ASSERT(src0->type == GGML_TYPE_F32);

                const int32_t p0 = ((const int32_t *)(dst->op_params))[0];
                const int32_t p1 = ((const int32_t *)(dst->op_params))[1];

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32].pipeline;

                ggml_metal_kargs_pad_reflect_1d args = {
                    /*.ne00 =*/ ne00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne0 =*/ ne0,
                    /*.ne1 =*/ ne1,
                    /*.ne2 =*/ ne2,
                    /*.ne3 =*/ ne3,
                    /*.nb0 =*/ nb0,
                    /*.nb1 =*/ nb1,
                    /*.nb2 =*/ nb2,
                    /*.nb3 =*/ nb3,
                    /*.p0 =*/ p0,
                    /*.p1 =*/ p1
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                [encoder setBytes:&args length:sizeof(args) atIndex:2];

                const int nth = MIN(1024, ne0);

                [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_ARANGE:
            {
                GGML_ASSERT(dst->type == GGML_TYPE_F32);

                float start;
                float step;

                memcpy(&start, ((const int32_t *) dst->op_params) + 0, sizeof(float));
                memcpy(&step,  ((const int32_t *) dst->op_params) + 2, sizeof(float));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline;

                ggml_metal_kargs_arange args = {
                    /*.ne0 =*/ ne0,
                    /*.start =*/ start,
                    /*.step =*/ step
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:0];
                [encoder setBytes:&args length:sizeof(args) atIndex:1];

                const int nth = MIN(1024, ne0);

                [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_TIMESTEP_EMBEDDING:
            {
                GGML_ASSERT(src0->type == GGML_TYPE_F32);

                const int dim        = dst->op_params[0];
                const int max_period = dst->op_params[1];

                const int half = dim / 2;

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline;

                ggml_metal_kargs_timestep_embedding args = {
                    /*.nb1 =*/ nb1,
                    /*.dim =*/ dim,
                    /*.max_period =*/ max_period
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                [encoder setBytes:&args length:sizeof(args) atIndex:2];

                const int nth = MIN(1024, half);

                [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_ARGSORT:
            {
                GGML_ASSERT(src0->type == GGML_TYPE_F32);
                GGML_ASSERT( dst->type == GGML_TYPE_I32);

                const int nrows = ggml_nrows(src0);

                enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];

                // bitonic sort requires the number of elements to be power of 2
                int64_t ne00_padded = 1;
                while (ne00_padded < ne00) {
                    ne00_padded *= 2;
                }

                // Metal kernels require the buffer size to be multiple of 16 bytes
                // https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength
                const int mem_size = GGML_PAD(ne00_padded*sizeof(int32_t), 16);

                id<MTLComputePipelineState> pipeline = nil;

                switch (order) {
                    case GGML_SORT_ORDER_ASC:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline;  break;
                    case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
                    default: GGML_ABORT("fatal error");
                };

                ggml_metal_kargs_argsort args = {
                    /*.ncols =*/ ne00,
                    /*.ncols_pad =*/ ne00_padded
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                [encoder setBytes:&args length:sizeof(args) atIndex:2];
                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];

                [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00_padded, 1, 1)];
            } break;
        case GGML_OP_LEAKY_RELU:
            {
                GGML_ASSERT(src0->type == GGML_TYPE_F32);

                float slope;
                memcpy(&slope, dst->op_params, sizeof(float));

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline;

                ggml_metal_kargs_leaky_relu args = {
                    /*.slope =*/ slope
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0   atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst    atIndex:1];
                [encoder setBytes:&args length:sizeof(args)   atIndex:2];

                const int64_t n = ggml_nelements(dst);

                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
            } break;
        case GGML_OP_FLASH_ATTN_EXT:
            {
                GGML_ASSERT(ne00 % 4  == 0);
                GGML_ASSERT(ne11 % 32 == 0);

                GGML_ASSERT(src0->type == GGML_TYPE_F32);
                GGML_ASSERT(src1->type == src2->type);

                //GGML_ASSERT(ggml_are_same_shape (src1, src2));
                GGML_ASSERT(ne11 == ne21);
                GGML_ASSERT(ne12 == ne22);

                struct ggml_tensor * src3 = node->src[3]; // mask
                struct ggml_tensor * src4 = node->src[4]; // sinks

                size_t offs_src3 = 0;
                size_t offs_src4 = 0;

                id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
                id<MTLBuffer> id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil;

                GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16);
                GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) &&
                        "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big");

                const int64_t  ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30);
                //const int64_t  ne31 = src3 ? src3->ne[1] : 0;
                const int64_t  ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32);
                const int64_t  ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33);

                const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30);
                const uint64_t nb31 = src3 ? src3->nb[1] : 0;
                const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32);
                const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33);

                float scale;
                float max_bias;
                float logit_softcap;
                memcpy(&scale,         ((const int32_t *) dst->op_params) + 0, sizeof(scale));
                memcpy(&max_bias,      ((const int32_t *) dst->op_params) + 1, sizeof(max_bias));
                memcpy(&logit_softcap, ((const int32_t *) dst->op_params) + 2, sizeof(logit_softcap));

                if (logit_softcap != 0.0f) {
                    scale /= logit_softcap;
                }

                const uint32_t n_head      = src0->ne[2];
                const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));

                const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
                const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

                id<MTLComputePipelineState> pipeline = nil;

                bool use_vec_kernel = false;

                // TODO: add vec kernels for (ne00%64 == 0) and maybe also for (ne00%32 == 0)
                //       for now avoiding mainly to keep the number of templates/kernels a bit lower
                //       these are now trivial to add after: https://github.com/ggml-org/llama.cpp/pull/12612
                if (ne01 >= 20 || (ne00%128 != 0 && ne00 != 64 && ne00 != 96 && ne00 != 192 && ne00 != 576)) {
                    switch (src1->type) {
                        case GGML_TYPE_F16:
                            {
                                if (ne00 == 192 && ne20 == 128) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK192_HV128].pipeline;
                                } else if (ne00 == 576 && ne20 == 512) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK576_HV512].pipeline;
                                } else {
                                    switch (ne00) {
                                        case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break;
                                        case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break;
                                        case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break;
                                        case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break;
                                        case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break;
                                        case 192: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H192].pipeline; break;
                                        case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
                                        default:
                                                  {
                                                      GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
                                                      GGML_LOG_ERROR("add template specialization for this size\n");
                                                      GGML_ABORT("add template specialization for this size");
                                                  }
                                    }
                                }
                            } break;
                        case GGML_TYPE_BF16:
                            {
                                if (ne00 == 192 && ne20 == 128) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK192_HV128].pipeline;
                                } else if (ne00 == 576 && ne20 == 512) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK576_HV512].pipeline;
                                } else {
                                    switch (ne00) {
                                        case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64 ].pipeline; break;
                                        case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80 ].pipeline; break;
                                        case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96 ].pipeline; break;
                                        case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112].pipeline; break;
                                        case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128].pipeline; break;
                                        case 192: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H192].pipeline; break;
                                        case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256].pipeline; break;
                                        default:
                                                  {
                                                      GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
                                                      GGML_LOG_ERROR("add template specialization for this size\n");
                                                      GGML_ABORT("add template specialization for this size");
                                                  }
                                    }
                                }
                            } break;
                        case GGML_TYPE_Q4_0:
                            {
                                if (ne00 == 192 && ne20 == 128) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK192_HV128].pipeline;
                                } else if (ne00 == 576 && ne20 == 512) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK576_HV512].pipeline;
                                } else {
                                    switch (ne00) {
                                        case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64 ].pipeline; break;
                                        case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80 ].pipeline; break;
                                        case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96 ].pipeline; break;
                                        case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112].pipeline; break;
                                        case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128].pipeline; break;
                                        case 192: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H192].pipeline; break;
                                        case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256].pipeline; break;
                                        default:
                                                  {
                                                      GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
                                                      GGML_LOG_ERROR("add template specialization for this size\n");
                                                      GGML_ABORT("add template specialization for this size");
                                                  }
                                    }
                                }
                            } break;
                        case GGML_TYPE_Q4_1:
                            {
                                if (ne00 == 192 && ne20 == 128) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK192_HV128].pipeline;
                                } else if (ne00 == 576 && ne20 == 512) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK576_HV512].pipeline;
                                } else {
                                    switch (ne00) {
                                        case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64 ].pipeline; break;
                                        case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80 ].pipeline; break;
                                        case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96 ].pipeline; break;
                                        case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112].pipeline; break;
                                        case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128].pipeline; break;
                                        case 192: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H192].pipeline; break;
                                        case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256].pipeline; break;
                                        default:
                                                  {
                                                      GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
                                                      GGML_LOG_ERROR("add template specialization for this size\n");
                                                      GGML_ABORT("add template specialization for this size");
                                                  }
                                    }
                                }
                            } break;
                        case GGML_TYPE_Q5_0:
                            {
                                if (ne00 == 192 && ne20 == 128) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK192_HV128].pipeline;
                                } else if (ne00 == 576 && ne20 == 512) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK576_HV512].pipeline;
                                } else {
                                    switch (ne00) {
                                        case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64 ].pipeline; break;
                                        case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80 ].pipeline; break;
                                        case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96 ].pipeline; break;
                                        case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112].pipeline; break;
                                        case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128].pipeline; break;
                                        case 192: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H192].pipeline; break;
                                        case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256].pipeline; break;
                                        default:
                                                  {
                                                      GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
                                                      GGML_LOG_ERROR("add template specialization for this size\n");
                                                      GGML_ABORT("add template specialization for this size");
                                                  }
                                    }
                                }
                            } break;
                        case GGML_TYPE_Q5_1:
                            {
                                if (ne00 == 192 && ne20 == 128) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK192_HV128].pipeline;
                                } else if (ne00 == 576 && ne20 == 512) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK576_HV512].pipeline;
                                } else {
                                    switch (ne00) {
                                        case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64 ].pipeline; break;
                                        case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80 ].pipeline; break;
                                        case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96 ].pipeline; break;
                                        case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112].pipeline; break;
                                        case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128].pipeline; break;
                                        case 192: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H192].pipeline; break;
                                        case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256].pipeline; break;
                                        default:
                                                  {
                                                      GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
                                                      GGML_LOG_ERROR("add template specialization for this size\n");
                                                      GGML_ABORT("add template specialization for this size");
                                                  }
                                    }
                                }
                            } break;
                        case GGML_TYPE_Q8_0:
                            {
                                if (ne00 == 192 && ne20 == 128) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128].pipeline;
                                } else if (ne00 == 576 && ne20 == 512) {
                                    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK576_HV512].pipeline;
                                } else {
                                    switch (ne00) {
                                        case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64 ].pipeline; break;
                                        case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80 ].pipeline; break;
                                        case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96 ].pipeline; break;
                                        case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112].pipeline; break;
                                        case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128].pipeline; break;
                                        case 192: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H192].pipeline; break;
                                        case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256].pipeline; break;
                                        default:
                                                  {
                                                      GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
                                                      GGML_LOG_ERROR("add template specialization for this size\n");
                                                      GGML_ABORT("add template specialization for this size");
                                                  }
                                    }
                                }
                            } break;
                        default:
                            {
                                GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
                                GGML_LOG_ERROR("add template specialization for this type\n");
                                GGML_ABORT("add template specialization for this type");
                            }
                    }
                } else {
                    use_vec_kernel = true;

                    switch (ne00) {
                        case 64:
                            {
                                switch (src1->type) {
                                    case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H64].pipeline; break;
                                    case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H64].pipeline; break;
                                    case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H64].pipeline; break;
                                    case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H64].pipeline; break;
                                    case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H64].pipeline; break;
                                    case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H64].pipeline; break;
                                    case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H64].pipeline; break;
                                    default:
                                        {
                                            GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
                                            GGML_LOG_ERROR("add template specialization for this type\n");
                                            GGML_ABORT("add template specialization for this type");
                                        }
                                }
                            } break;
                        case 96:
                            {
                                switch (src1->type) {
                                    case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H96].pipeline; break;
                                    case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H96].pipeline; break;
                                    case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H96].pipeline; break;
                                    case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H96].pipeline; break;
                                    case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H96].pipeline; break;
                                    case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H96].pipeline; break;
                                    case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H96].pipeline; break;
                                    default:
                                        {
                                            GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
                                            GGML_LOG_ERROR("add template specialization for this type\n");
                                            GGML_ABORT("add template specialization for this type");
                                        }
                                }
                            } break;
                        case 128:
                            {
                                switch (src1->type) {
                                    case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break;
                                    case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128].pipeline; break;
                                    case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128].pipeline; break;
                                    case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128].pipeline; break;
                                    case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128].pipeline; break;
                                    case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128].pipeline; break;
                                    case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128].pipeline; break;
                                    default:
                                        {
                                            GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
                                            GGML_LOG_ERROR("add template specialization for this type\n");
                                            GGML_ABORT("add template specialization for this type");
                                        }
                                }
                            } break;
                        case 192:
                            {
                                if (ne20 == 128) {
                                    switch (src1->type) {
                                        case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_HK192_HV128].pipeline; break;
                                        case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_HK192_HV128].pipeline; break;
                                        case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_HK192_HV128].pipeline; break;
                                        case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_HK192_HV128].pipeline; break;
                                        case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_HK192_HV128].pipeline; break;
                                        case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_HK192_HV128].pipeline; break;
                                        case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_HK192_HV128].pipeline; break;
                                        default:
                                            {
                                                GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
                                                GGML_LOG_ERROR("add template specialization for this type\n");
                                                GGML_ABORT("add template specialization for this type");
                                            }
                                    }
                                } else {
                                    switch (src1->type) {
                                        case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H192].pipeline; break;
                                        case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H192].pipeline; break;
                                        case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H192].pipeline; break;
                                        case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H192].pipeline; break;
                                        case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H192].pipeline; break;
                                        case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H192].pipeline; break;
                                        case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H192].pipeline; break;
                                        default:
                                            {
                                                GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
                                                GGML_LOG_ERROR("add template specialization for this type\n");
                                                GGML_ABORT("add template specialization for this type");
                                            }
                                    }
                                }
                            } break;
                        case 256:
                            {
                                switch (src1->type) {
                                    case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
                                    case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256].pipeline; break;
                                    case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256].pipeline; break;
                                    case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256].pipeline; break;
                                    case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256].pipeline; break;
                                    case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256].pipeline; break;
                                    case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256].pipeline; break;
                                    default:
                                        {
                                            GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
                                            GGML_LOG_ERROR("add template specialization for this type\n");
                                            GGML_ABORT("add template specialization for this type");
                                        }
                                }
                            } break;
                        case 576:
                            {
                                if (ne20 == 512) {
                                    switch (src1->type) {
                                        case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_HK576_HV512].pipeline; break;
                                        case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_HK576_HV512].pipeline; break;
                                        case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_HK576_HV512].pipeline; break;
                                        case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_HK576_HV512].pipeline; break;
                                        case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_HK576_HV512].pipeline; break;
                                        case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_HK576_HV512].pipeline; break;
                                        case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_HK576_HV512].pipeline; break;
                                        default:
                                            {
                                                GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
                                                GGML_LOG_ERROR("add template specialization for this type\n");
                                                GGML_ABORT("add template specialization for this type");
                                            }
                                    }
                                } else {
                                    GGML_LOG_ERROR("unsupported size: %lld\n", ne20);
                                    GGML_LOG_ERROR("add template specialization for this size\n");
                                    GGML_ABORT("add template specialization for this size");
                                }
                            } break;
                        default:
                            {
                                GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
                                GGML_LOG_ERROR("add template specialization for this size\n");
                                GGML_ABORT("add template specialization for this size");
                            }
                    }
                }

                ggml_metal_kargs_flash_attn_ext args = {
                    /*.ne01          =*/ ne01,
                    /*.ne02          =*/ ne02,
                    /*.ne03          =*/ ne03,
                    /*.nb01          =*/ nb01,
                    /*.nb02          =*/ nb02,
                    /*.nb03          =*/ nb03,
                    /*.ne11          =*/ ne11,
                    /*.ne_12_2       =*/ ne12,
                    /*.ne_12_3       =*/ ne13,
                    /*.nb11          =*/ nb11,
                    /*.nb12          =*/ nb12,
                    /*.nb13          =*/ nb13,
                    /*.nb21          =*/ nb21,
                    /*.nb22          =*/ nb22,
                    /*.nb23          =*/ nb23,
                    /*.ne32          =*/ ne32,
                    /*.ne33          =*/ ne33,
                    /*.nb31          =*/ nb31,
                    /*.nb32          =*/ nb32,
                    /*.nb33          =*/ nb33,
                    /*.ne1           =*/ ne1,
                    /*.ne2           =*/ ne2,
                    /*.scale         =*/ scale,
                    /*.max_bias      =*/ max_bias,
                    /*.m0            =*/ m0,
                    /*.m1            =*/ m1,
                    /*.n_head_log2   =*/ n_head_log2,
                    /*.logit_softcap =*/ logit_softcap,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args)     atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0     atIndex:1];
                [encoder setBuffer:id_src1 offset:offs_src1     atIndex:2];
                [encoder setBuffer:id_src2 offset:offs_src2     atIndex:3];
                if (id_src3) {
                    [encoder setBuffer:id_src3 offset:offs_src3 atIndex:4];
                } else {
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:4];
                }
                if (id_src4) {
                    [encoder setBuffer:id_src4 offset:offs_src4 atIndex:5];
                } else {
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:5];
                }
                [encoder setBuffer:id_dst offset:offs_dst       atIndex:6];

                if (!use_vec_kernel) {
                    // half8x8 kernel
                    const int64_t nqptg = 8;  // queries per threadgroup    !! sync with kernel template arguments !!
                    const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!

                    GGML_ASSERT(nqptg <= 32);
                    GGML_ASSERT(nqptg  % 8  == 0);
                    GGML_ASSERT(ncpsg  % 32 == 0);

                    const int is_q = ggml_is_quantized(src1->type) ? 1 : 0;

                    // 2*(2*ncpsg + nqptg)*(nsg)
                    // ncpsg soft_max values + ncpsg mask values + a diagonal scaling matrix (in float)
                    //
                    // 16*32*(nsg)
                    // the shared memory needed for the simdgroups to load the KV cache
                    // each thread loads (dequantizes) 16 head elements, there are 32 threads in th SG
                    //
 #define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(2*ne00 + 2*(2*ncpsg + nqptg)*(nsg)) + is_q*(16*32*(nsg)))*(sizeof(float)/2), 16))

                    int64_t nsgmax = 2;

                    while (true) {
                        const size_t smem = FATTN_SMEM(nsgmax);
                        if (smem > device.maxThreadgroupMemoryLength) {
                            break;
                        }
                        nsgmax *= 2;
                    }
                    nsgmax /= 2;

                    // simdgroups per threadgroup (a.k.a. warps)
                    const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4;

                    const size_t smem = FATTN_SMEM(nsg);

                    //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg);
                    GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength);
                    [encoder setThreadgroupMemoryLength:smem atIndex:0];
 #undef FATTN_SMEM
                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
                } else {
                    // half4x4 kernel
                    const int64_t nqptg = 1;  // queries per threadgroup    !! sync with kernel template arguments !!
                    const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!

                    GGML_ASSERT(nqptg <= 32);
                    GGML_ASSERT(nqptg  % 1  == 0);
                    GGML_ASSERT(ncpsg  % 32 == 0);

                    // ne00 + 2*ncpsg*(nsg)
                    // for each query, we load it as f16 in shared memory (ne00)
                    // and store the soft_max values and the mask
                    //
                    // ne00*(nsg)
                    // each simdgroup has a full f32 head vector in shared mem to accumulate results
                    //
 #define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(GGML_PAD(ne00, 128) + 4*ncpsg*(nsg)) + 2*ne20*(nsg))*(sizeof(float)/2), 16))

                    int64_t nsgmax = 2;
                    while (true) {
                        const size_t smem = FATTN_SMEM(nsgmax);
                        if (smem > device.maxThreadgroupMemoryLength) {
                            break;
                        }
                        nsgmax *= 2;
                    }
                    nsgmax /= 2;

                    // simdgroups per threadgroup (a.k.a. warps)
                    const int64_t nsgt = MAX(2, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)));

                    int64_t nsg = 1;
                    while (nsg <= nsgt) {
                        nsg *= 2;
                    }
                    nsg /= 2;

                    const size_t smem = FATTN_SMEM(nsg);

                    //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg);
                    GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength);
                    [encoder setThreadgroupMemoryLength:smem atIndex:0];
 #undef FATTN_SMEM
                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
                }
            } break;
        case GGML_OP_DUP:
        case GGML_OP_CPY:
        case GGML_OP_CONT:
            {
                id<MTLComputePipelineState> pipeline = nil;

                switch (src0t) {
                    case GGML_TYPE_F32:
                        {
                            GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0);

                            switch (dstt) {
                                case GGML_TYPE_F32:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break;
                                case GGML_TYPE_F16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break;
                                case GGML_TYPE_BF16:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_BF16].pipeline; break;
                                case GGML_TYPE_Q8_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break;
                                case GGML_TYPE_Q4_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break;
                                case GGML_TYPE_Q4_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break;
                                case GGML_TYPE_Q5_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break;
                                case GGML_TYPE_Q5_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break;
                                case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            };
                        } break;
                    case GGML_TYPE_F16:
                        {
                            switch (dstt) {
                                case GGML_TYPE_F32:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break;
                                case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            };
                        } break;
                    case GGML_TYPE_BF16:
                        {
                            switch (dstt) {
                                case GGML_TYPE_F32:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_BF16_F32].pipeline; break;
                                case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            };
                        } break;
                    case GGML_TYPE_Q4_0:
                        {
                            switch (dstt) {
                                case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q4_0_F32].pipeline; break;
                                case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q4_0_F16].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            };
                        } break;
                    case GGML_TYPE_Q4_1:
                        {
                            switch (dstt) {
                                case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q4_1_F32].pipeline; break;
                                case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q4_1_F16].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            };
                        } break;
                    case GGML_TYPE_Q5_0:
                        {
                            switch (dstt) {
                                case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q5_0_F32].pipeline; break;
                                case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q5_0_F16].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            };
                        } break;
                    case GGML_TYPE_Q5_1:
                        {
                            switch (dstt) {
                                case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q5_1_F32].pipeline; break;
                                case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q5_1_F16].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            };
                        } break;
                    case GGML_TYPE_Q8_0:
                        {
                            switch (dstt) {
                                case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q8_0_F32].pipeline; break;
                                case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_Q8_0_F16].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            };
                        } break;
                    default: GGML_ABORT("not implemented");
                }

                GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);

                // TODO: support
                //const int32_t nk00 = ne00/ggml_blck_size(dst->type);
                const int32_t nk00 = ne00;

                int nth = 32; // SIMD width

                while (nth < nk00 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
                    nth *= 2;
                }

                nth = MIN(nth, (int) pipeline.maxTotalThreadsPerThreadgroup);

                // when rows are small, we can batch them together in a single threadgroup
                int nrptg = 1;

                // TODO: relax this constraint in the future
                if (ggml_blck_size(src0->type) == 1 && ggml_blck_size(dst->type) == 1) {
                    if (nth > nk00) {
                        nrptg = (nth + nk00 - 1)/nk00;
                        nth   = nk00;

                        if (nrptg*nth > (int) pipeline.maxTotalThreadsPerThreadgroup) {
                            nrptg--;
                        }
                    }
                }

                nth = MIN(nth, nk00);

                ggml_metal_kargs_cpy args = {
                    /*.ne00 =*/ nk00,
                    /*.ne01 =*/ ne01,
                    /*.ne02 =*/ ne02,
                    /*.ne03 =*/ ne03,
                    /*.nb00 =*/ nb00,
                    /*.nb01 =*/ nb01,
                    /*.nb02 =*/ nb02,
                    /*.nb03 =*/ nb03,
                    /*.ne0  =*/ ne0,
                    /*.ne1  =*/ ne1,
                    /*.ne2  =*/ ne2,
                    /*.ne3  =*/ ne3,
                    /*.nb0  =*/ nb0,
                    /*.nb1  =*/ nb1,
                    /*.nb2  =*/ nb2,
                    /*.nb3  =*/ nb3,
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];

                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nrptg - 1)/nrptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, nrptg, 1)];
            } break;
        case GGML_OP_SET:
            {
                GGML_ASSERT(ggml_are_same_shape(src0, dst));
                GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));

                // src0 and dst as viewed during set
                const size_t dst_nb0 = ggml_element_size(src0);

                const size_t dst_nb1 = ((int32_t *) dst->op_params)[0];
                const size_t dst_nb2 = ((int32_t *) dst->op_params)[1];
                const size_t dst_nb3 = ((int32_t *) dst->op_params)[2];
                const size_t offset  = ((int32_t *) dst->op_params)[3];
                const bool   inplace = (bool) ((int32_t *) dst->op_params)[4];

                if (!inplace) {
                    memcpy(((char *)  dst->data), ((char *) src0->data), ggml_nbytes(dst));
                }

                const int im0 = (ne10 == 0 ? 0 : ne10-1);
                const int im1 = (ne11 == 0 ? 0 : ne11-1);
                const int im2 = (ne12 == 0 ? 0 : ne12-1);
                const int im3 = (ne13 == 0 ? 0 : ne13-1);

                GGML_ASSERT(offset + im0*dst_nb0  + im1*dst_nb1  + im2*dst_nb2  + im3*dst_nb3  <= ggml_nbytes(dst));

                id<MTLComputePipelineState> pipeline = nil;

                switch (src0t) {
                    case GGML_TYPE_F32:
                        GGML_ASSERT(nb10 == sizeof(float));
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_F32].pipeline; break;
                    case GGML_TYPE_I32:
                        GGML_ASSERT(nb10 == sizeof(int32_t));
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_I32].pipeline; break;
                    default: GGML_ABORT("fatal error");
                }

                ggml_metal_kargs_set args = {
                    /*.ne10    =*/ ne10,
                    /*.ne11    =*/ ne11,
                    /*.ne12    =*/ ne12,
                    /*.nb10    =*/ nb10,
                    /*.nb11    =*/ nb11,
                    /*.nb12    =*/ nb12,
                    /*.nb13    =*/ nb13,
                    /*.nb1     =*/ dst_nb1,
                    /*.nb2     =*/ dst_nb2,
                    /*.nb3     =*/ dst_nb3,
                    /*.offs    =*/ offset,
                    /*.inplace =*/ inplace,
                };

                const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne10);

                [encoder setComputePipelineState:pipeline];
                [encoder setBytes:&args    length:sizeof(args) atIndex:0];
                [encoder setBuffer:id_src0 offset:offs_src0    atIndex:1];
                [encoder setBuffer:id_src1 offset:offs_src1    atIndex:2];
                [encoder setBuffer:id_dst  offset:offs_dst     atIndex:3];

                [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
        case GGML_OP_POOL_2D:
            {
                GGML_ASSERT(ggml_is_contiguous(src0));
                GGML_ASSERT(src0t == GGML_TYPE_F32 && src0t == dstt);

                const int32_t * opts = dst->op_params;
                enum ggml_op_pool op = opts[0];

                id<MTLComputePipelineState> pipeline = nil;
                switch (src0t) {
                    case GGML_TYPE_F32: {
                        switch(op) {
                            case GGML_OP_POOL_AVG:
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32].pipeline; break;
                            case GGML_OP_POOL_MAX:
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32].pipeline; break;
                            default: GGML_ASSERT(false && "not implemented");
                        }
                    } break;
                    default: GGML_ASSERT(false && "not implemented");
                }

                const int32_t k0 = opts[1];
                const int32_t k1 = opts[2];
                const int32_t s0 = opts[3];
                const int32_t s1 = opts[4];
                const int32_t p0 = opts[5];
                const int32_t p1 = opts[6];

                const int64_t IH = src0->ne[1];
                const int64_t IW = src0->ne[0];

                const int64_t N  = dst->ne[3];
                const int64_t OC = dst->ne[2];
                const int64_t OH = dst->ne[1];
                const int64_t OW = dst->ne[0];

                const int64_t parallel_elements = N * OC * OH * OW;
                const int64_t n_threads = MIN((int64_t)[pipeline maxTotalThreadsPerThreadgroup], parallel_elements);
                const int64_t n_tg = (parallel_elements + n_threads - 1) / n_threads;

                ggml_metal_kargs_pool_2d args_pool_2d = {
                    /* .k0 = */ k0,
                    /* .k1 = */ k1,
                    /* .s0 = */ s0,
                    /* .s1 = */ s1,
                    /* .p0 = */ p0,
                    /* .p1 = */ p1,
                    /* .IH = */ IH,
                    /* .IW = */ IW,
                    /* .OH = */ OH,
                    /* .OW = */ OW,
                    /* .parallel_elements = */ parallel_elements
                };

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                [encoder setBytes:&args_pool_2d length:sizeof(args_pool_2d) atIndex:2];

                [encoder dispatchThreadgroups:MTLSizeMake(n_tg, 1, 1) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)];
            } break;
            case GGML_OP_ARGMAX:
            {
                GGML_ASSERT(src0->type == GGML_TYPE_F32);
                GGML_ASSERT(ggml_is_contiguous_1(src0));
                GGML_ASSERT(nb00 == ggml_type_size(src0->type));

                const int64_t nrows = ggml_nrows(src0);

                int nth = 32; // SIMD width
                while (nth < ne00 && nth*ne01*ne02*ne03 < 256) {
                    nth *= 2;
                }

                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGMAX].pipeline;

                [encoder setComputePipelineState:pipeline];
                [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
                [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
                [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
                [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
                [encoder setThreadgroupMemoryLength:32*sizeof(float)   atIndex:0];
                [encoder setThreadgroupMemoryLength:32*sizeof(int32_t) atIndex:1];

                [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
            } break;
       default:
            {
                GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));
                GGML_ABORT("fatal error");
            }
    }

    return n_fuse;
 }

 static enum ggml_status ggml_metal_graph_compute(
            ggml_backend_t   backend,
        struct ggml_cgraph * gf) {
    struct ggml_backend_metal_context        * ctx     = backend->context;
    struct ggml_backend_metal_device_context * ctx_dev = backend->device->context;

    // number of nodes encoded by the main thread (empirically determined)
    const int n_main = 128;

    // number of threads in addition to the main thread
    const int n_cb = ctx->n_cb;

    // submit the ggml compute graph to the GPU by creating command buffers and encoding the ops in them
    // the first n_nodes_0 are encoded and submitted for processing directly by the calling thread
    // while these nodes are processing, we start n_cb threads to enqueue the rest of the nodes
    // each thread creates it's own command buffer and enqueues the ops in parallel
    //
    // tests on M1 Pro and M2 Ultra using LLaMA models, show that optimal values for n_cb are 1 or 2

    @autoreleasepool {
        ctx->gf = gf;

        ctx->n_nodes_0 = MIN(n_main, gf->n_nodes);
        ctx->n_nodes_1 = gf->n_nodes - ctx->n_nodes_0;

        ctx->n_nodes_per_cb = (ctx->n_nodes_1 + ctx->n_cb - 1) / ctx->n_cb;

        const bool should_capture = ctx->capture_next_compute;
        if (should_capture) {
            ctx->capture_next_compute = false;

            if (!ctx->capture_started) {
                // create capture scope
                ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:ctx_dev->mtl_device];

                MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new];
                descriptor.captureObject = ctx->capture_scope;
                descriptor.destination = MTLCaptureDestinationGPUTraceDocument;
                descriptor.outputURL = [NSURL fileURLWithPath:[NSString stringWithFormat:@"/tmp/perf-metal.gputrace"]];

                NSError * error = nil;
                if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) {
                    GGML_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]);
                } else {
                    [ctx->capture_scope beginScope];
                    ctx->capture_started = true;
                }
            }
        }

        // the main thread commits the first few commands immediately
        // cmd_buf[n_cb]
        {
            id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
            ctx->cmd_bufs[n_cb].obj = cmd_buf;

            [cmd_buf enqueue];
            ctx->encode_async(n_cb);
        }

        // prepare the rest of the command buffers asynchronously
        // cmd_buf[0.. n_cb)
        for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
            id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
            ctx->cmd_bufs[cb_idx].obj = cmd_buf;

            // always enqueue the first two command buffers
            // enqueue all of the command buffers if we don't need to abort
            if (cb_idx < 2 || ctx->abort_callback == NULL) {
                [cmd_buf enqueue];
            }
        }

        dispatch_apply(n_cb, ctx->d_queue, ctx->encode_async);

        // wait for completion and check status of each command buffer
        // needed to detect if the device ran out-of-memory for example (#1881)
        {
            id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[n_cb].obj;
            [cmd_buf waitUntilCompleted];

            MTLCommandBufferStatus status = [cmd_buf status];
            if (status != MTLCommandBufferStatusCompleted) {
                GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, n_cb, status);
                if (status == MTLCommandBufferStatusError) {
                    GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
                }

                return GGML_STATUS_FAILED;
            }
        }

        for (int i = 0; i < n_cb; ++i) {
            id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[i].obj;
            [cmd_buf waitUntilCompleted];

            MTLCommandBufferStatus status = [cmd_buf status];
            if (status != MTLCommandBufferStatusCompleted) {
                GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
                if (status == MTLCommandBufferStatusError) {
                    GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
                }

                return GGML_STATUS_FAILED;
            }

            id<MTLCommandBuffer> next_buffer = (i + 1 < n_cb ? ctx->cmd_bufs[i + 1].obj : nil);
            if (!next_buffer) {
                continue;
            }

            const bool next_queued = ([next_buffer status] != MTLCommandBufferStatusNotEnqueued);
            if (next_queued) {
                continue;
            }

            if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) {
                GGML_LOG_INFO("%s: command buffer %d aborted", __func__, i);
                return GGML_STATUS_ABORTED;
            }

            [next_buffer commit];
        }

        if (!should_capture && ctx->capture_started) {
            [ctx->capture_scope endScope];
            [[MTLCaptureManager sharedCaptureManager] stopCapture];
        }
    }

    return GGML_STATUS_SUCCESS;
 }

 ////////////////////////////////////////////////////////////////////////////////

 // backend interface

 static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;

    for (int i = 0; i < ctx->n_buffers; i++) {
        [ctx->buffers[i].metal release];
    }

    ggml_backend_metal_buffer_rset_free(ctx);

    if (ctx->owned) {
 #if TARGET_OS_OSX
        vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ctx->all_data, ctx->all_size);
 #else
        free(ctx->all_data);
 #endif
    }

    free(ctx);
 }

 static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
    struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;

    return ctx->all_data;
 }

 static void ggml_backend_metal_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
    memset((char *)tensor->data + offset, value, size);

    GGML_UNUSED(buffer);
 }

 static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    memcpy((char *)tensor->data + offset, data, size);

    GGML_UNUSED(buffer);
 }

 static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    memcpy(data, (const char *)tensor->data + offset, size);

    GGML_UNUSED(buffer);
 }

 static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
    if (ggml_backend_buffer_is_host(src->buffer)) {
        memcpy(dst->data, src->data, ggml_nbytes(src));
        return true;
    }
    return false;

    GGML_UNUSED(buffer);
 }

 static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
    struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;

    memset(ctx->all_data, value, ctx->all_size);
 }

 static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
    /* .free_buffer     = */ ggml_backend_metal_buffer_free_buffer,
    /* .get_base        = */ ggml_backend_metal_buffer_get_base,
    /* .init_tensor     = */ NULL,
    /* .memset_tensor   = */ ggml_backend_metal_buffer_memset_tensor,
    /* .set_tensor      = */ ggml_backend_metal_buffer_set_tensor,
    /* .get_tensor      = */ ggml_backend_metal_buffer_get_tensor,
    /* .cpy_tensor      = */ ggml_backend_metal_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_metal_buffer_clear,
    /* .reset           = */ NULL,
 };

 // default buffer type

 static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
    return "Metal";

    GGML_UNUSED(buft);
 }

 static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t size_aligned) {
 #ifndef GGML_METAL_NDEBUG
 #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
    if (@available(macOS 10.12, iOS 16.0, *)) {
        GGML_LOG_DEBUG("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)\n",
                __func__,
                size_aligned / 1024.0 / 1024.0,
                device.currentAllocatedSize / 1024.0 / 1024.0,
                device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);

        if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) {
            GGML_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
        }
    } else {
        GGML_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n",
                __func__,
                size_aligned / 1024.0 / 1024.0,
                device.currentAllocatedSize / 1024.0 / 1024.0);
    }
 #endif
 #endif
    GGML_UNUSED(device);
    GGML_UNUSED(size_aligned);
 }

 static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
    struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context));

    const size_t size_page = sysconf(_SC_PAGESIZE);

    size_t size_aligned = size;
    if ((size_aligned % size_page) != 0) {
        size_aligned += (size_page - (size_aligned % size_page));
    }

    struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)buft->device->context;

    GGML_ASSERT(ctx_dev->mtl_device != nil);

    id<MTLDevice> device = ctx_dev->mtl_device;

    ctx->all_data = ggml_metal_host_malloc(size_aligned);
    ctx->all_size = size_aligned;
    ctx->owned = true;
    ctx->n_buffers = 1;

    if (ctx->all_data != NULL) {
        ctx->buffers[0].data  = ctx->all_data;
        ctx->buffers[0].size  = size;
        ctx->buffers[0].metal = nil;

        if (size_aligned > 0) {
            {
                id<MTLBuffer> __src = [device newBufferWithBytesNoCopy:ctx->all_data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
                id<MTLBuffer> __dst = nil;
                
                bool __force_private = getenv("GGML_METAL_FORCE_PRIVATE") != NULL;
 #if TARGET_OS_OSX
                bool __is_discrete = !device.hasUnifiedMemory;
 #else
                bool __is_discrete = false;
 #endif
                if (__src && (__is_discrete || __force_private)) {
                    __dst = [device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate];
                    id<MTLCommandQueue> __q = [device newCommandQueue];
                    id<MTLCommandBuffer> __cb = [__q commandBuffer];
                    id<MTLBlitCommandEncoder> __blit = [__cb blitCommandEncoder];
                    [__blit copyFromBuffer:__src sourceOffset:0 toBuffer:__dst destinationOffset:0 size:size_aligned];
                    [__blit endEncoding];
                    [__cb commit];
                    [__cb waitUntilCompleted];
                    [__q release];
                    [__src release];
                    __src = nil;
                    ctx->buffers[0].metal = __dst;
                } else {
                    ctx->buffers[0].metal = __src;
                }
            }
        }
    }

    if (size_aligned > 0 && (ctx->all_data == NULL || ctx->buffers[0].metal == nil)) {
        GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
        free(ctx);
        return NULL;
    }

    if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
        GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
        free(ctx);
        return NULL;
    }

    //ggml_backend_metal_log_allocated_size(device, size_aligned);

    return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size);
 }

 static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
    return 32;

    GGML_UNUSED(buft);
 }

 static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
    const size_t max_size = ((struct ggml_backend_metal_device_context *)buft->device->context)->max_size;

    return max_size;
 }

 static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
    return true;

    GGML_UNUSED(buft);
 }

 ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
    static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = {
        /* .iface = */ {
            /* .get_name         = */ ggml_backend_metal_buffer_type_get_name,
            /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_alloc_buffer,
            /* .get_alignment    = */ ggml_backend_metal_buffer_type_get_alignment,
            /* .get_max_size     = */ ggml_backend_metal_buffer_type_get_max_size,
            /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
            /* .is_host          = */ ggml_backend_metal_buffer_type_is_host,
        },
        /* .device  = */ &g_ggml_backend_metal_device,
        /* .context = */ NULL,
    };

    return &ggml_backend_buffer_type_metal;
 }

 static const char * ggml_backend_metal_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) {
    return "Metal_Mapped";

    GGML_UNUSED(buft);
 }

 static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void) {
    static struct ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_metal = {
        /* .iface = */ {
            /* .get_name         = */ ggml_backend_metal_buffer_from_ptr_type_get_name,
            /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_alloc_buffer,
            /* .get_alignment    = */ ggml_backend_metal_buffer_type_get_alignment,
            /* .get_max_size     = */ ggml_backend_metal_buffer_type_get_max_size,
            /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
            /* .is_host          = */ ggml_backend_metal_buffer_type_is_host,
        },
        /* .device  = */ &g_ggml_backend_metal_device,
        /* .context = */ NULL,
    };

    return &ggml_backend_buffer_from_ptr_type_metal;
 }

 // TODO: obsoleted by ggml_backend_metal_device_buffer_from_ptr
 ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
    struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context));

    ctx->all_data = data;
    ctx->all_size = size;
    ctx->owned = false;
    ctx->n_buffers = 0;

    const size_t size_page = sysconf(_SC_PAGESIZE);

    // page-align the data ptr
    {
        const uintptr_t offs = (uintptr_t) data % size_page;
        data  = (void *) ((char *) data - offs);
        size += offs;
    }

    size_t size_aligned = size;
    if ((size_aligned % size_page) != 0) {
        size_aligned += (size_page - (size_aligned % size_page));
    }

    struct ggml_backend_metal_device_context * ctx_dev = &g_ggml_ctx_dev_main;

    GGML_ASSERT(ctx_dev->mtl_device != nil);

    id<MTLDevice> device = ctx_dev->mtl_device;

    // the buffer fits into the max buffer size allowed by the device
    if (size_aligned <= device.maxBufferLength) {
        ctx->buffers[ctx->n_buffers].data  = data;
        ctx->buffers[ctx->n_buffers].size  = size;
        ctx->buffers[ctx->n_buffers].metal = nil;

        if (size_aligned > 0) {
            {

                id<MTLBuffer> __src = [device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
                id<MTLBuffer> __dst = nil;
        
                bool __force_private = getenv("GGML_METAL_FORCE_PRIVATE") != NULL;
 #if TARGET_OS_OSX
                bool __is_discrete = !device.hasUnifiedMemory;
 #else
                bool __is_discrete = false;
 #endif
                if (__src && (__is_discrete || __force_private)) {
                    __dst = [device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate];
                    id<MTLCommandQueue> __q = [device newCommandQueue];
                    id<MTLCommandBuffer> __cb = [__q commandBuffer];
                    id<MTLBlitCommandEncoder> __blit = [__cb blitCommandEncoder];
                    [__blit copyFromBuffer:__src sourceOffset:0 toBuffer:__dst destinationOffset:0 size:size_aligned];
                    [__blit endEncoding];
                    [__cb commit];
                    [__cb waitUntilCompleted];
                    [__q release];
                    [__src release];
                    __src = nil;
                    ctx->buffers[ctx->n_buffers].metal = __dst;
                } else {
                    ctx->buffers[ctx->n_buffers].metal = __src;
                }
            
 }

            if (ctx->buffers[ctx->n_buffers].metal == nil) {
                GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
                return false;
            }
        }

        ggml_backend_metal_log_allocated_size(device, size_aligned);

        ++ctx->n_buffers;
    } else {
        // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into
        // one of the views
        const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case
        const size_t size_step = device.maxBufferLength - size_ovlp;
        const size_t size_view = device.maxBufferLength;

        for (size_t i = 0; i < size; i += size_step) {
            const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i);

            ctx->buffers[ctx->n_buffers].data  = (void *) ((uint8_t *) data + i);
            ctx->buffers[ctx->n_buffers].size  = size_step_aligned;
            ctx->buffers[ctx->n_buffers].metal = nil;

            if (size_step_aligned > 0) {
                    {
        id<MTLBuffer> __src = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
        id<MTLBuffer> __dst = nil;
        
        bool __force_private = getenv("GGML_METAL_FORCE_PRIVATE") != NULL;
 #if TARGET_OS_OSX
        bool __is_discrete = !device.hasUnifiedMemory;
 #else
        bool __is_discrete = false;
 #endif
        if (__src && (__is_discrete || __force_private)) {
            __dst = [device newBufferWithLength:size_step_aligned options:MTLResourceStorageModePrivate];
            id<MTLCommandQueue> __q = [device newCommandQueue];
            id<MTLCommandBuffer> __cb = [__q commandBuffer];
            id<MTLBlitCommandEncoder> __blit = [__cb blitCommandEncoder];
            [__blit copyFromBuffer:__src sourceOffset:0 toBuffer:__dst destinationOffset:0 size:size_step_aligned];
            [__blit endEncoding];
            [__cb commit];
            [__cb waitUntilCompleted];
            [__q release];
            [__src release];
            ctx->buffers[ctx->n_buffers].metal = __dst;
        } else {
            ctx->buffers[ctx->n_buffers].metal = __src;
        }
    }

                if (ctx->buffers[ctx->n_buffers].metal == nil) {
                    GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0);
                    return false;
                }
            }

            ggml_backend_metal_log_allocated_size(device, size_step_aligned);

            if (i + size_step < size) {
                GGML_LOG_INFO("\n");
            }

            ++ctx->n_buffers;
        }
    }

    if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
        GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
        free(ctx);
        return NULL;
    }

    return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size);
 }

 // backend

 static const char * ggml_backend_metal_name(ggml_backend_t backend) {
    return "Metal";

    GGML_UNUSED(backend);
 }

 static void ggml_backend_metal_free(ggml_backend_t backend) {
    struct ggml_backend_metal_context * ctx = backend->context;

    ggml_metal_free(ctx);

    free(backend);
 }

 static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
    return ggml_metal_graph_compute(backend, cgraph);
 }

 static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
    GGML_ASSERT(ggml_backend_is_metal(backend));

    struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context;

    if (ctx->n_cb != n_cb) {
        ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_COMMAND_BUFFERS);

        if (ctx->n_cb > 2) {
            GGML_LOG_WARN("%s: n_cb = %d, using n_cb > 2 is not recommended and can degrade the performance in some cases\n", __func__, n_cb);
        }
    }

    if (ctx->encode_async) {
        Block_release(ctx->encode_async);
    }

    ctx->encode_async = Block_copy(^(size_t iter) {
        const int cb_idx = iter;
        const int n_cb_l = ctx->n_cb;

        const int n_nodes_0 = ctx->n_nodes_0;
        const int n_nodes_1 = ctx->n_nodes_1;

        const int n_nodes_per_cb = ctx->n_nodes_per_cb;

        id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[cb_idx].obj;

        id<MTLComputeCommandEncoder> encoder = [cmd_buf computeCommandEncoder];

        int node_start = 0;
        int node_end   = n_nodes_0;

        if (cb_idx < n_cb_l) {
            node_start = n_nodes_0 + (                                         (cb_idx + 0) * n_nodes_per_cb);
            node_end   = n_nodes_0 + (MIN((cb_idx == n_cb_l - 1) ? n_nodes_1 : (cb_idx + 1) * n_nodes_per_cb, n_nodes_1));
        }

        const bool should_capture = ctx->capture_next_compute;

        struct ggml_metal_mem_pool * mem_pool = ctx->cmd_bufs[cb_idx].mem_pool;
        ggml_metal_mem_pool_reset(mem_pool);

        for (int idx = node_start; idx < node_end;) {
            if (should_capture) {
                [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(ggml_graph_node(ctx->gf, idx)) encoding:NSUTF8StringEncoding]];
            }

            const int res = ggml_metal_encode_node(backend, idx, node_end, encoder, mem_pool);
            if (idx + res > node_end) {
                GGML_ABORT("fusion error: nodes spanning multiple encoders have been fused. this indicates a bug in the fusion logic %s",
                        "https://github.com/ggml-org/llama.cpp/pull/14849");
            }

            if (should_capture) {
                [encoder popDebugGroup];
            }

            if (res == 0) {
                break;
            }

            idx += res;
        }

        [encoder endEncoding];

        if (cb_idx < 2 || ctx->abort_callback == NULL) {
            [cmd_buf commit];
        }
    
 });
 }
 static struct ggml_backend_i ggml_backend_metal_i = {
    /* .get_name                = */ ggml_backend_metal_name,
    /* .free                    = */ ggml_backend_metal_free,
    /* .set_tensor_async        = */ NULL,
    /* .get_tensor_async        = */ NULL,
    /* .cpy_tensor_async        = */ NULL,
    /* .synchronize             = */ NULL,
    /* .graph_plan_create       = */ NULL,
    /* .graph_plan_free         = */ NULL,
    /* .graph_plan_update       = */ NULL,
    /* .graph_plan_compute      = */ NULL,
    /* .graph_compute           = */ ggml_backend_metal_graph_compute,
    /* .event_record            = */ NULL,
    /* .event_wait              = */ NULL,
 };

 static ggml_guid_t ggml_backend_metal_guid(void) {
    static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 };
    return &guid;
 }

 // TODO: remove in the future
 ggml_backend_t ggml_backend_metal_init(void) {
    ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_metal_reg(), 0);

    struct ggml_backend_metal_context * ctx = ggml_metal_init(dev);
    if (ctx == NULL) {
        GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
        return NULL;
    }

    ggml_backend_t backend = malloc(sizeof(struct ggml_backend));

    *backend = (struct ggml_backend) {
        /* .guid      = */ ggml_backend_metal_guid(),
        /* .interface = */ ggml_backend_metal_i,
        /* .device    = */ dev,
        /* .context   = */ ctx,
    };

                       {
 #if TARGET_OS_OSX
                       int __ncb = 0;
                       const char * __env_ncb = getenv("GGML_METAL_N_CB");
                       if (__env_ncb) { __ncb = atoi(__env_ncb); }
                       if (__ncb <= 0) {
                           id<MTLDevice> __dev = ((struct ggml_backend_metal_device_context *)dev->context)->mtl_device;
                           bool __is_discrete = !__dev.hasUnifiedMemory;
                           __ncb = __is_discrete ? 2 : 1;
                       }
                       ggml_backend_metal_set_n_cb(backend, __ncb);
 #else
                       ggml_backend_metal_set_n_cb(backend, 1);
 #endif
                   }

    return backend;
 }

 bool ggml_backend_is_metal(ggml_backend_t backend) {
    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid());
 }

 void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data) {
    GGML_ASSERT(ggml_backend_is_metal(backend));

    struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context;

    ctx->abort_callback = abort_callback;
    ctx->abort_callback_data = user_data;
 }

 bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
    GGML_ASSERT(ggml_backend_is_metal(backend));

    struct ggml_backend_metal_device_context * ctx_dev = backend->device->context;

    GGML_ASSERT(ctx_dev->mtl_device != nil);

    return [ctx_dev->mtl_device supportsFamily:(MTLGPUFamilyApple1 + family - 1)];
 }

 void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) {
    GGML_ASSERT(ggml_backend_is_metal(backend));

    struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context;
    ctx->capture_next_compute = true;
 }

 // backend device

 static const char * ggml_backend_metal_device_get_name(ggml_backend_dev_t dev) {
    return "Metal";

    GGML_UNUSED(dev);
 }

 static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t dev) {
    struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context;

    return ctx_dev->name;
 }

 static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
    if (@available(macOS 10.12, iOS 16.0, *)) {
        struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context;
        id<MTLDevice> device = ctx_dev->mtl_device;

        *total = device.recommendedMaxWorkingSetSize;
        *free  = *total - device.currentAllocatedSize;
    } else {
        *free = 1;
        *total = 1;
    }
 }

 static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backend_dev_t dev) {
    return GGML_BACKEND_DEVICE_TYPE_GPU;

    GGML_UNUSED(dev);
 }

 static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
    props->name        = ggml_backend_metal_device_get_name(dev);
    props->description = ggml_backend_metal_device_get_description(dev);
    props->type        = ggml_backend_metal_device_get_type(dev);
    ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total);
    props->caps = (struct ggml_backend_dev_caps) {
        /* .async                 = */ false,
        /* .host_buffer           = */ false,
        /* .buffer_from_host_ptr  = */ true,
        /* .events                = */ false,
    };
 }

 static ggml_backend_t ggml_backend_metal_device_init(ggml_backend_dev_t dev, const char * params) {
    struct ggml_backend_metal_context * ctx = ggml_metal_init(dev);
    if (ctx == NULL) {
        GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
        return NULL;
    }

    ggml_backend_t backend = malloc(sizeof(struct ggml_backend));

    *backend = (struct ggml_backend) {
        /* .guid      = */ ggml_backend_metal_guid(),
        /* .interface = */ ggml_backend_metal_i,
        /* .device    = */ dev,
        /* .context   = */ ctx,
    };

                       {
 #if TARGET_OS_OSX
                       int __ncb = 0;
                       const char * __env_ncb = getenv("GGML_METAL_N_CB");
                       if (__env_ncb) { __ncb = atoi(__env_ncb); }
                       if (__ncb <= 0) {
                           id<MTLDevice> __dev = ((struct ggml_backend_metal_device_context *)dev->context)->mtl_device;
                           bool __is_discrete = !__dev.hasUnifiedMemory;
                           __ncb = __is_discrete ? 2 : 1;
                       }
                       ggml_backend_metal_set_n_cb(backend, __ncb);
 #else
                       ggml_backend_metal_set_n_cb(backend, 1);
 #endif
                   }

    return backend;

    GGML_UNUSED(params);
 }

 static ggml_backend_buffer_type_t ggml_backend_metal_device_get_buffer_type(ggml_backend_dev_t dev) {
    return ggml_backend_metal_buffer_type();

    GGML_UNUSED(dev);
 }

 static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
    struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context));

    ctx->all_data = ptr;
    ctx->all_size = size;
    ctx->owned = false;
    ctx->n_buffers = 0;

    const size_t size_page = sysconf(_SC_PAGESIZE);

    // page-align the data ptr
    {
        const uintptr_t offs = (uintptr_t) ptr % size_page;
        ptr  = (void *) ((char *) ptr - offs);
        size += offs;
    }

    size_t size_aligned = size;
    if ((size_aligned % size_page) != 0) {
        size_aligned += (size_page - (size_aligned % size_page));
    }

    struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context;

    GGML_ASSERT(ctx_dev->mtl_device != nil);

    id<MTLDevice> device = ctx_dev->mtl_device;

    // the buffer fits into the max buffer size allowed by the device
    if (size_aligned <= device.maxBufferLength) {
        ctx->buffers[ctx->n_buffers].data  = ptr;
        ctx->buffers[ctx->n_buffers].size  = size;
        ctx->buffers[ctx->n_buffers].metal = nil;

        if (size_aligned > 0) {
            {

                id<MTLBuffer> __src = [device newBufferWithBytesNoCopy:ptr length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
                id<MTLBuffer> __dst = nil;
                bool __force_private = getenv("GGML_METAL_FORCE_PRIVATE") != NULL;
 #if TARGET_OS_OSX
                bool __is_discrete = !device.hasUnifiedMemory;
 #else
                bool __is_discrete = false;
 #endif
                if (__src && (__is_discrete || __force_private)) {
                    __dst = [device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate];
                    id<MTLCommandQueue> __q = [device newCommandQueue];
                    id<MTLCommandBuffer> __cb = [__q commandBuffer];
                    id<MTLBlitCommandEncoder> __blit = [__cb blitCommandEncoder];
                    [__blit copyFromBuffer:__src sourceOffset:0 toBuffer:__dst destinationOffset:0 size:size_aligned];
                    [__blit endEncoding];
                    [__cb commit];
                    [__cb waitUntilCompleted];
                    [__q release];
                    [__src release];
                    __src = nil;
                    ctx->buffers[ctx->n_buffers].metal = __dst;
                } else {
                    ctx->buffers[ctx->n_buffers].metal = __src;
                }
            
 }

            if (ctx->buffers[ctx->n_buffers].metal == nil) {
                GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
                return false;
            }
        }

        ggml_backend_metal_log_allocated_size(device, size_aligned);

        ++ctx->n_buffers;
    } else {
        // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into
        // one of the views
        const size_t size_ovlp = ((max_tensor_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case
        const size_t size_step = device.maxBufferLength - size_ovlp;
        const size_t size_view = device.maxBufferLength;

        for (size_t i = 0; i < size; i += size_step) {
            const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i);

            ctx->buffers[ctx->n_buffers].data  = (void *) ((uint8_t *) ptr + i);
            ctx->buffers[ctx->n_buffers].size  = size_step_aligned;
            ctx->buffers[ctx->n_buffers].metal = nil;

            if (size_step_aligned > 0) {
                    {
        id<MTLBuffer> __src = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) ptr + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
        id<MTLBuffer> __dst = nil;
        bool __force_private = getenv("GGML_METAL_FORCE_PRIVATE") != NULL;
 #if TARGET_OS_OSX
        bool __is_discrete = !device.hasUnifiedMemory;
 #else
        bool __is_discrete = false;
 #endif
        if (__src && (__is_discrete || __force_private)) {
            __dst = [device newBufferWithLength:size_step_aligned options:MTLResourceStorageModePrivate];
            id<MTLCommandQueue> __q = [device newCommandQueue];
            id<MTLCommandBuffer> __cb = [__q commandBuffer];
            id<MTLBlitCommandEncoder> __blit = [__cb blitCommandEncoder];
            [__blit copyFromBuffer:__src sourceOffset:0 toBuffer:__dst destinationOffset:0 size:size_step_aligned];
            [__blit endEncoding];
            [__cb commit];
            [__cb waitUntilCompleted];
            [__q release];
            [__src release];
            ctx->buffers[ctx->n_buffers].metal = __dst;
        } else {
            ctx->buffers[ctx->n_buffers].metal = __src;
        }
    }

                if (ctx->buffers[ctx->n_buffers].metal == nil) {
                    GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0);
                    return false;
                }
            }

            ggml_backend_metal_log_allocated_size(device, size_step_aligned);

            if (i + size_step < size) {
                GGML_LOG_INFO("\n");
            }

            ++ctx->n_buffers;
        }
    }

    if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
        GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
        free(ctx);
        return NULL;
    }

    return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size);
 }

 static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
    struct ggml_backend_metal_device_context * ctx_dev = dev->context;

    return ggml_metal_supports_op(ctx_dev, op);
 }

 static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
    return
        buft->iface.get_name == ggml_backend_metal_buffer_type_get_name ||
        buft->iface.get_name == ggml_backend_metal_buffer_from_ptr_type_get_name;

    GGML_UNUSED(dev);
 }

 static bool ggml_backend_metal_device_offload_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
    return false;

    GGML_UNUSED(dev);
    GGML_UNUSED(op);
 }

 static struct ggml_backend_device_i ggml_backend_metal_device_i = {
    /* .get_name             = */ ggml_backend_metal_device_get_name,
    /* .get_description      = */ ggml_backend_metal_device_get_description,
    /* .get_memory           = */ ggml_backend_metal_device_get_memory,
    /* .get_type             = */ ggml_backend_metal_device_get_type,
    /* .get_props            = */ ggml_backend_metal_device_get_props,
    /* .init_backend         = */ ggml_backend_metal_device_init,
    /* .get_buffer_type      = */ ggml_backend_metal_device_get_buffer_type,
    /* .get_host_buffer_type = */ NULL,
    /* .buffer_from_host_ptr = */ ggml_backend_metal_device_buffer_from_ptr,
    /* .supports_op          = */ ggml_backend_metal_device_supports_op,
    /* .supports_buft        = */ ggml_backend_metal_device_supports_buft,
    /* .offload_op           = */ ggml_backend_metal_device_offload_op,
    /* .event_new            = */ NULL,
    /* .event_free           = */ NULL,
    /* .event_synchronize    = */ NULL,
 };

 // backend registry

 static const char * ggml_backend_metal_reg_get_name(ggml_backend_reg_t reg) {
    return "Metal";

    GGML_UNUSED(reg);
 }

 static size_t ggml_backend_metal_reg_device_count(ggml_backend_reg_t reg) {
    return 1;

    GGML_UNUSED(reg);
 }

 static ggml_backend_dev_t ggml_backend_metal_reg_device_get(ggml_backend_reg_t reg, size_t index) {
    GGML_ASSERT(index == 0);

    return &g_ggml_backend_metal_device;

    GGML_UNUSED(reg);
    GGML_UNUSED(index);
 }

 static struct ggml_backend_feature g_ggml_backend_metal_features[] = {
 #if defined(GGML_METAL_EMBED_LIBRARY)
    { "EMBED_LIBRARY", "1" },
 #endif
 #if defined(GGML_METAL_USE_BF16)
    { "BF16", "1" },
 #endif
    { nil, nil },
 };

 static struct ggml_backend_feature * ggml_backend_metal_get_features(ggml_backend_reg_t reg) {
    return g_ggml_backend_metal_features;

    GGML_UNUSED(reg);
 }

 static void * ggml_backend_metal_get_proc_address(ggml_backend_reg_t reg, const char * name) {
    if (strcmp(name, "ggml_backend_get_features") == 0) {
        return (void *)ggml_backend_metal_get_features;
    }

    return NULL;

    GGML_UNUSED(reg);
 }
 static struct ggml_backend_reg_i ggml_backend_metal_reg_i = {
    /* .get_name         = */ ggml_backend_metal_reg_get_name,
    /* .device_count     = */ ggml_backend_metal_reg_device_count,
    /* .device_get       = */ ggml_backend_metal_reg_device_get,
    /* .get_proc_address = */ ggml_backend_metal_get_proc_address,
 };

 // called upon program exit
 static void ggml_metal_cleanup(void) {
    ggml_backend_metal_device_rel(&g_ggml_ctx_dev_main);
 }

 // TODO: make thread-safe
 ggml_backend_reg_t ggml_backend_metal_reg(void) {
    ggml_backend_metal_device_acq(&g_ggml_ctx_dev_main);

    // register cleanup callback
    // TODO: not ideal, but not sure if there is a better way to do this in Objective-C
    atexit(ggml_metal_cleanup);

    {
        g_ggml_backend_metal_reg = (struct ggml_backend_reg) {
            /* .api_version = */ GGML_BACKEND_API_VERSION,
            /* .iface       = */ ggml_backend_metal_reg_i,
            /* .context     = */ NULL,
        };

        g_ggml_backend_metal_device = (struct ggml_backend_device) {
            /* .iface   = */ ggml_backend_metal_device_i,
            /* .reg     = */ &g_ggml_backend_metal_reg,
            /* .context = */ &g_ggml_ctx_dev_main,
        };
    }

    return &g_ggml_backend_metal_reg;
 }

 GGML_BACKEND_DL_IMPL(ggml_backend_metal_reg)
diff --git a/gistfile1.txt b/gistfile1.txt
 Despite Metal 3 support being available (enabled via cmake -B build -DGGML_METAL=ON), it remains highly limited and runs at a sluggish 0.8 tokens per second on the GPU (AMD 6900 XT), while surprisingly achieving 21 tokens per second on the CPU 12 core Xeon W.

 After three days of GPT 'torture', I've finally obtained a promising new Metal backend.

 What was slowing it down
 llama.cpp’s Metal backend was mapping model weights with Shared storage using newBufferWithBytesNoCopy(...). On discrete GPUs, that forces the GPU to read weights over PCIe from system RAM every time → big bandwidth bottleneck and high latency.

 Key modifications

 Pick the right GPU automatically (or by index)
 Replaced MTLCreateSystemDefaultDevice() with logic that:
 Lets you pick a device by index via GGML_METAL_DEVICE_INDEX, or
 Falls back to the device with the largest recommendedMaxWorkingSetSize (usually the best choice on multi-GPU Mac Pro).
 This prevents accidentally using the wrong die/GPU.

 Put weights in VRAM (Private) and copy once
 When the device doesn’t have unified memory (i.e., a discrete AMD GPU) or GGML_METAL_FORCE_PRIVATE=1:
 Create a Private MTLBuffer in VRAM.
 Do a one-time blit from the original Shared (host) buffer to the Private buffer.
 This was applied to both:
 The “single big buffer” path (buffers[0]), and
 The “from host pointer / sliced” path (buffer_from_ptr with ctx->n_buffers).
 Result: the GPU reads weights from VRAM during inference, avoiding repeated PCIe reads.

 More command-buffer parallelism on discrete GPUs
 The backend previously forced n_cb = 1.
 Now it sets n_cb = 2 by default on discrete GPUs (or you can override with GGML_METAL_N_CB).
 This increases submission concurrency a bit on dGPU hardware.

 Safety/cleanup fixes (build warnings/errors)
 Removed the earlier “block literal + comma operator” trick and replaced those lines with regular C/Obj-C blocks—no more left operand of comma operator has no effect warnings.
 Fixed the Block_copy(^(size_t iter){ ... }); region:
 Removed the temporary __out variable and rewired assignments cleanly.
 Balanced braces and added a newline at end of file to silence -Wnewline-eof.

 Motivation

 Et voila : ggml-metal-optimized-1.m

 ./build/bin/llama-bench -ngl 99 -m ../Models/llama-2-7b-q4_0.gguf

 model	size	params	backend	threads	test	t/s
 llama 7B Q4_0	3.56 GiB	6.74 B	Metal,BLAS	12	pp512	326.29 ± 0.64
 llama 7B Q4_0	3.56 GiB	6.74 B	Metal,BLAS	12	tg128	98.27 ± 0.80
 build: 79c1160 (custom) with the new Metal3 Backend patch ggml-metal-optimized-1.m						
 vs Vulkan Build

 ./build/bin/llama-bench -ngl 99 -m ../Models/llama-2-7b-q4_0.gguf -sm none -mg 0

 model	size	params	backend	threads	sm	test	t/s
 llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan,BLAS	12	none	pp512	643.37 ± 0.65
 llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan,BLAS	12	none	tg128	82.26 ± 0.20
 build: 79c1160 (6123)							
 Possible Implementation

 Environment variables you can use with this new patch Metal3 Backend
 GGML_METAL_DEVICE_INDEX — choose a specific GPU/die.
 GGML_METAL_FORCE_PRIVATE=1 — force VRAM (Private) even if macOS reports unified memory.
 GGML_METAL_N_CB=2 — tune the number of command buffers (2 is a good default on AMD dGPU).
	Despite Metal 3 support being available (enabled via cmake -B build -DGGML_METAL=ON), it remains highly limited and runs at a sluggish 0.8 tokens per second on the GPU (AMD 6900 XT), while surprisingly achieving 21 tokens per second on the CPU 12 core Xeon W.

	After three days of GPT 'torture', I've finally obtained a promising new Metal backend.

	What was slowing it down
	llama.cpp’s Metal backend was mapping model weights with Shared storage using newBufferWithBytesNoCopy(...). On discrete GPUs, that forces the GPU to read weights over PCIe from system RAM every time → big bandwidth bottleneck and high latency.

	Key modifications

	Pick the right GPU automatically (or by index)
	Replaced MTLCreateSystemDefaultDevice() with logic that:
	Lets you pick a device by index via GGML_METAL_DEVICE_INDEX, or
	Falls back to the device with the largest recommendedMaxWorkingSetSize (usually the best choice on multi-GPU Mac Pro).
	This prevents accidentally using the wrong die/GPU.

	Put weights in VRAM (Private) and copy once
	When the device doesn’t have unified memory (i.e., a discrete AMD GPU) or GGML_METAL_FORCE_PRIVATE=1:
	Create a Private MTLBuffer in VRAM.
	Do a one-time blit from the original Shared (host) buffer to the Private buffer.
	This was applied to both:
	The “single big buffer” path (buffers[0]), and
	The “from host pointer / sliced” path (buffer_from_ptr with ctx->n_buffers).
	Result: the GPU reads weights from VRAM during inference, avoiding repeated PCIe reads.

	More command-buffer parallelism on discrete GPUs
	The backend previously forced n_cb = 1.
	Now it sets n_cb = 2 by default on discrete GPUs (or you can override with GGML_METAL_N_CB).
	This increases submission concurrency a bit on dGPU hardware.

	Safety/cleanup fixes (build warnings/errors)
	Removed the earlier “block literal + comma operator” trick and replaced those lines with regular C/Obj-C blocks—no more left operand of comma operator has no effect warnings.
	Fixed the Block_copy(^(size_t iter){ ... }); region:
	Removed the temporary __out variable and rewired assignments cleanly.
	Balanced braces and added a newline at end of file to silence -Wnewline-eof.

	Motivation

	Et voila : ggml-metal-optimized-1.m

	./build/bin/llama-bench -ngl 99 -m ../Models/llama-2-7b-q4_0.gguf

	model size params backend threads test t/s
	llama 7B Q4_0 3.56 GiB 6.74 B Metal,BLAS 12 pp512 326.29 ± 0.64
	llama 7B Q4_0 3.56 GiB 6.74 B Metal,BLAS 12 tg128 98.27 ± 0.80
	build: 79c1160 (custom) with the new Metal3 Backend patch ggml-metal-optimized-1.m
	vs Vulkan Build

	./build/bin/llama-bench -ngl 99 -m ../Models/llama-2-7b-q4_0.gguf -sm none -mg 0

	model size params backend threads sm test t/s
	llama 7B Q4_0 3.56 GiB 6.74 B Vulkan,BLAS 12 none pp512 643.37 ± 0.65
	llama 7B Q4_0 3.56 GiB 6.74 B Vulkan,BLAS 12 none tg128 82.26 ± 0.20
	build: 79c1160 (6123)
	Possible Implementation

	Environment variables you can use with this new patch Metal3 Backend
	GGML_METAL_DEVICE_INDEX — choose a specific GPU/die.
	GGML_METAL_FORCE_PRIVATE=1 — force VRAM (Private) even if macOS reports unified memory.
	GGML_METAL_N_CB=2 — tune the number of command buffers (2 is a good default on AMD dGPU).
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